User:Shawndouglas/sandbox/sublevel4 - Revision history

Shawndouglas at 19:33, 17 February 2023

2023-02-17T19:33:49Z

← Older revision		Revision as of 19:33, 17 February 2023
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	:'''Medical diagnostics''': AI is used for the formulation of reference ranges, improvement of quality control, and automated interpretation of results. "Continuous monitoring of specimen acceptability, collection and transport can result in the prompt identification and correction of problems, leading to improved patient care and a reduction in unnecessary redraws and delays in reporting results."<ref name=":13" />		:'''Medical diagnostics''': AI is used for the formulation of reference ranges, improvement of quality control, and automated interpretation of results. "Continuous monitoring of specimen acceptability, collection and transport can result in the prompt identification and correction of problems, leading to improved patient care and a reduction in unnecessary redraws and delays in reporting results."<ref name=":13" />
	:'''Reproduction science''': "The field of AI is the marriage of humans and computers while reproductive medicine combines clinical medicine and the scientific laboratory of embryology. The application of AI has the potential to disconnect healthcare professionals from patients through algorithms, automated communication, and clinical imaging. However, in the embryology laboratory, AI, with its focus on gametes and embryos, can avoid the same risk of distancing from the patient. Areas of application of AI in the laboratory would be to enhance and automate embryo ranking through analysis of images, the ultimate goal being to predict successful implantation. Might such a trend obviate the need for embryo morphological assessment, time-lapse imaging and preimplantation genetic testing for aneuploidy (PGT-A), including mosaicism. Additionally, AI could assist with automation through analysis of testicular sperm samples searching for viable gametes, embryo grading uniformity."<ref name=":9" />		:'''Reproduction science''': "The field of AI is the marriage of humans and computers while reproductive medicine combines clinical medicine and the scientific laboratory of embryology. The application of AI has the potential to disconnect healthcare professionals from patients through algorithms, automated communication, and clinical imaging. However, in the embryology laboratory, AI, with its focus on gametes and embryos, can avoid the same risk of distancing from the patient. Areas of application of AI in the laboratory would be to enhance and automate embryo ranking through analysis of images, the ultimate goal being to predict successful implantation. Might such a trend obviate the need for embryo morphological assessment, time-lapse imaging and preimplantation genetic testing for aneuploidy (PGT-A), including mosaicism. Additionally, AI could assist with automation through analysis of testicular sperm samples searching for viable gametes, embryo grading uniformity."<ref name=":9" />
	:'''Chromatography-heavy sciences''': " A great example of this is AI in the Liquid Chromatography Mass Spectrometry (LC-MS) field.4 LC-MS is a great tool used to measure various compounds in the human body, including everything from hormone levels to trace metals. One of the ways AI has already integrated with LC-MS is how it cuts down on the rate limiting steps of LC-MS, which more often than not are sample prep and LC separations. One system that Physicians Lab has made use of is parallel processing using SCIEX MPX 2.0 High Throughput System. This system can couple parallel runs with one LCMS instrument, resulting in twice the speed with no loss to accuracy. It can do this by staggering two runs either using the same method, or different methods entirely. What really makes this system great is its ability to automatically detect carryover and inject solvent blanks to clean the instrument. The system will then continue its analyzing, while automatically reinjecting samples that may be affected by the carryover. It will also flag high concentration without user input, allowing for easy detection of possibly faulty samples. This allows it to operate without users from startup to shut down. Some of the other ways that it can be used to increase efficiency are by using integrated network features to work on anything from streamlining management to increased throughput."<ref name="AlbanoCal19" />		:'''Chromatography-heavy sciences''': " A great example of this is AI in the Liquid Chromatography Mass Spectrometry (LC-MS) field. LC-MS is a great tool used to measure various compounds in the human body, including everything from hormone levels to trace metals. One of the ways AI has already integrated with LC-MS is how it cuts down on the rate limiting steps of LC-MS, which more often than not are sample prep and LC separations. One system that Physicians Lab has made use of is parallel processing using SCIEX MPX 2.0 High Throughput System. This system can couple parallel runs with one LCMS instrument, resulting in twice the speed with no loss to accuracy. It can do this by staggering two runs either using the same method, or different methods entirely. What really makes this system great is its ability to automatically detect carryover and inject solvent blanks to clean the instrument. The system will then continue its analyzing, while automatically reinjecting samples that may be affected by the carryover. It will also flag high concentration without user input, allowing for easy detection of possibly faulty samples. This allows it to operate without users from startup to shut down. Some of the other ways that it can be used to increase efficiency are by using integrated network features to work on anything from streamlining management to increased throughput."<ref name="AlbanoCal19" />
	:'''Most any lab''': "Predictive analytics, for example, is one tool that the Pistoia Alliance is using to better understand laboratory instruments and how they might fail over time... With the right data management strategies and careful consideration of metadata, how to best store data so that it can be used in future AI and ML workflows is essential to the pursuit of AI in the laboratory. Utilizing technologies such as LIMS and ELN enables lab users to catalogue data, providing context and instrument parameters that can then be fed into AI or ML systems. Without the correct data or with mismatched data types, AI and ML will not be possible, or at the very least, could provide undue bias trying to compare data from disparate sources."<ref>{{Cite web \|date=29 January 2021 \|title=Data Analytics \|work=Scientific Computing World - Building a Smart Laboratory 2020 \|url=https://www.scientific-computing.com/feature/data-analytics-0 \|publisher=Europa Science Ltd \|accessdate=17 February 2023}}</ref>		:'''Most any lab''': "Predictive analytics, for example, is one tool that the Pistoia Alliance is using to better understand laboratory instruments and how they might fail over time... With the right data management strategies and careful consideration of metadata, how to best store data so that it can be used in future AI and ML workflows is essential to the pursuit of AI in the laboratory. Utilizing technologies such as LIMS and ELN enables lab users to catalogue data, providing context and instrument parameters that can then be fed into AI or ML systems. Without the correct data or with mismatched data types, AI and ML will not be possible, or at the very least, could provide undue bias trying to compare data from disparate sources."<ref>{{Cite web \|date=29 January 2021 \|title=Data Analytics \|work=Scientific Computing World - Building a Smart Laboratory 2020 \|url=https://www.scientific-computing.com/feature/data-analytics-0 \|publisher=Europa Science Ltd \|accessdate=17 February 2023}}</ref>
	:'''Most any lab''': "When the actionable items are automatically created by Optima, the 'engine' starts working. An extremely sophisticated algorithm is able to assign the tasks to the resources, both laboratory personnel and instruments, according to the system configuration. Optima, thanks to a large amount of time dedicated to research the best way to automate this critical process, is able to automate most of the lab resource scheduling."<ref>{{Cite web \|last=Optima Team \|date=15 December 2020 \|title=The concept of machine learning applied to lab resources scheduling \|work=Optima Blog \|url=https://www.optima.life/blog/the-concept-of-machine-learning-applied-to-lab-resources-scheduling/ \|publisher=Optima PLC Tracking Tools S.L \|accessdate=17 February 2023}}</ref>		:'''Most any lab''': "When the actionable items are automatically created by Optima, the 'engine' starts working. An extremely sophisticated algorithm is able to assign the tasks to the resources, both laboratory personnel and instruments, according to the system configuration. Optima, thanks to a large amount of time dedicated to research the best way to automate this critical process, is able to automate most of the lab resource scheduling."<ref>{{Cite web \|last=Optima Team \|date=15 December 2020 \|title=The concept of machine learning applied to lab resources scheduling \|work=Optima Blog \|url=https://www.optima.life/blog/the-concept-of-machine-learning-applied-to-lab-resources-scheduling/ \|publisher=Optima PLC Tracking Tools S.L \|accessdate=17 February 2023}}</ref>

Shawndouglas at 19:25, 17 February 2023

2023-02-17T19:25:11Z

Show changes

Shawndouglas at 18:39, 17 February 2023

2023-02-17T18:39:51Z

← Older revision		Revision as of 18:39, 17 February 2023
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	:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web \|url=https://siim.org/page/22w_clinical_adoption_of_ai \|title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties \|author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. \|publisher=Society for Imaging Informatics in Medicine \|date=11 October 2022}}</ref>		:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web \|url=https://siim.org/page/22w_clinical_adoption_of_ai \|title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties \|author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. \|publisher=Society for Imaging Informatics in Medicine \|date=11 October 2022}}</ref>
	:ophthalmology clinics<ref>{{Cite journal \|last=He \|first=Mingguang \|last2=Li \|first2=Zhixi \|last3=Liu \|first3=Chi \|last4=Shi \|first4=Danli \|last5=Tan \|first5=Zachary \|date=2020-07 \|title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge \|url=https://journals.lww.com/10.1097/APO.0000000000000301 \|journal=Asia-Pacific Journal of Ophthalmology \|language=en \|volume=9 \|issue=4 \|pages=299–307 \|doi=10.1097/APO.0000000000000301 \|issn=2162-0989}}</ref>		:ophthalmology clinics<ref>{{Cite journal \|last=He \|first=Mingguang \|last2=Li \|first2=Zhixi \|last3=Liu \|first3=Chi \|last4=Shi \|first4=Danli \|last5=Tan \|first5=Zachary \|date=2020-07 \|title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge \|url=https://journals.lww.com/10.1097/APO.0000000000000301 \|journal=Asia-Pacific Journal of Ophthalmology \|language=en \|volume=9 \|issue=4 \|pages=299–307 \|doi=10.1097/APO.0000000000000301 \|issn=2162-0989}}</ref>
	:reproduction clinics<ref>{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref><ref name="ESHREArti22">{{cite web \|url=https://www.focusonreproduction.eu/article/ESHRE-News-22AI \|title=Annual Meeting 2022: Artificial intelligence in embryology and ART \|author=European Society of Human Reproduction and Embryology \|work=Focus on Reproduction \|date=06 July 2022 \|accessdate=16 February 2023}}</ref><ref name="HinckleyApply21">{{cite web \|url=https://rscbayarea.com/blog/applying-ai-for-better-ivf-success \|title=Applying AI (Artificial Intelligence) in the Lab for Better IVF Success \|author=Hinckley, M. \|work=Reproductive Science Center Blog \|publisher=Reproductive Science Center of the Bay Area \|date=17 March 2021 \|accessdate=16 February 2023}}</ref>		:reproduction clinics<ref name=":9">{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref><ref name="ESHREArti22">{{cite web \|url=https://www.focusonreproduction.eu/article/ESHRE-News-22AI \|title=Annual Meeting 2022: Artificial intelligence in embryology and ART \|author=European Society of Human Reproduction and Embryology \|work=Focus on Reproduction \|date=06 July 2022 \|accessdate=16 February 2023}}</ref><ref name="HinckleyApply21">{{cite web \|url=https://rscbayarea.com/blog/applying-ai-for-better-ivf-success \|title=Applying AI (Artificial Intelligence) in the Lab for Better IVF Success \|author=Hinckley, M. \|work=Reproductive Science Center Blog \|publisher=Reproductive Science Center of the Bay Area \|date=17 March 2021 \|accessdate=16 February 2023}}</ref>
	:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>		:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>
	:material testing labs<ref name=":2">{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref name=":3">{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>		:material testing labs<ref name=":2">{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref name=":3">{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>
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	:'''Medical diagnostics''': Development and implementation of [[Clinical decision support system\|clinical decision support systems]] <ref name=":0" /><ref name=":1" />		:'''Medical diagnostics''': Development and implementation of [[Clinical decision support system\|clinical decision support systems]] <ref name=":0" /><ref name=":1" />
	:'''Medical diagnostics''': "Finally, in the laboratory, AI reduces the number of unnecessary blood samples when diagnosing infection. Instead of the 'gold standard blood sample' that takes 24-72 hours, the algorithm can predict the outcome of the blood sample with almost 80% accuracy based on demographics, vital signs, medications, and laboratory and radiology results. These are all examples of how Artificial Intelligence can be used to test better and faster with information that already exists. This saves time and costs."<ref name=":5" />		:'''Medical diagnostics''': "Finally, in the laboratory, AI reduces the number of unnecessary blood samples when diagnosing infection. Instead of the 'gold standard blood sample' that takes 24-72 hours, the algorithm can predict the outcome of the blood sample with almost 80% accuracy based on demographics, vital signs, medications, and laboratory and radiology results. These are all examples of how Artificial Intelligence can be used to test better and faster with information that already exists. This saves time and costs."<ref name=":5" />
	:Medical diagnostics: "Chang sees two overarching classes of AI models: those that tackle internal challenges in the lab, such as how to deliver more accurate results to clinicians; and those that seek to identify cohorts of patients and care processes to close quality gaps in health delivery systems. The lab, however, 'isn’t truly an island,' said Michelle Stoffel, MD, PhD, associate chief medical information officer for laboratory medicine and pathology at M Health Fairview and the University of Minnesota in Minneapolis. 'When other healthcare professionals are working with electronic health records or other applications, there could be AI-driven tools, or algorithms used by an institution’s systems that may draw on laboratory data.'"<ref name=":8" />		:'''Medical diagnostics''': "Chang sees two overarching classes of AI models: those that tackle internal challenges in the lab, such as how to deliver more accurate results to clinicians; and those that seek to identify cohorts of patients and care processes to close quality gaps in health delivery systems. The lab, however, 'isn’t truly an island,' said Michelle Stoffel, MD, PhD, associate chief medical information officer for laboratory medicine and pathology at M Health Fairview and the University of Minnesota in Minneapolis. 'When other healthcare professionals are working with electronic health records or other applications, there could be AI-driven tools, or algorithms used by an institution’s systems that may draw on laboratory data.'"<ref name=":8" />
			:'''Reproduction science''': "The field of AI is the marriage of humans and computers while reproductive medicine combines clinical medicine and the scientific laboratory of embryology. The application of AI has the potential to disconnect healthcare professionals from patients through algorithms, automated communication, and clinical imaging. However, in the embryology laboratory, AI, with its focus on gametes and embryos, can avoid the same risk of distancing from the patient. Areas of application of AI in the laboratory would be to enhance and automate embryo ranking through analysis of images, the ultimate goal being to predict successful implantation. Might such a trend obviate the need for embryo morphological assessment, time-lapse imaging and preimplantation genetic testing for aneuploidy (PGT-A), including mosaicism. Additionally, AI could assist with automation through analysis of testicular sperm samples searching for viable gametes, embryo grading uniformity."<ref name=":9" />
			:'''Chromatography-heavy sciences''': " A great example of this is AI in the Liquid Chromatography Mass Spectrometry (LC-MS) field.4 LC-MS is a great tool used to measure various compounds in the human body, including everything from hormone levels to trace metals. One of the ways AI has already integrated with LC-MS is how it cuts down on the rate limiting steps of LC-MS, which more often than not are sample prep and LC separations. One system that Physicians Lab has made use of is parallel processing using SCIEX MPX 2.0 High Throughput System. This system can couple parallel runs with one LCMS instrument, resulting in twice the speed with no loss to accuracy. It can do this by staggering two runs either using the same method, or different methods entirely. What really makes this system great is its ability to automatically detect carryover and inject solvent blanks to clean the instrument. The system will then continue its analyzing, while automatically reinjecting samples that may be affected by the carryover. It will also flag high concentration without user input, allowing for easy detection of possibly faulty samples. This allows it to operate without users from startup to shut down. Some of the other ways that it can be used to increase efficiency are by using integrated network features to work on anything from streamlining management to increased throughput."<ref name="AlbanoCal19" />
	:'''Most any lab''': "Predictive analytics, for example, is one tool that the Pistoia Alliance is using to better understand laboratory instruments and how they might fail over time... With the right data management strategies and careful consideration of metadata, how to best store data so that it can be used in future AI and ML workflows is essential to the pursuit of AI in the laboratory. Utilizing technologies such as LIMS and ELN enables lab users to catalogue data, providing context and instrument parameters that can then be fed into AI or ML systems. Without the correct data or with mismatched data types, AI and ML will not be possible, or at the very least, could provide undue bias trying to compare data from disparate sources."<ref>{{Cite web \|date=29 January 2021 \|title=Data Analytics \|work=Scientific Computing World - Building a Smart Laboratory 2020 \|url=https://www.scientific-computing.com/feature/data-analytics-0 \|publisher=Europa Science Ltd \|accessdate=17 February 2023}}</ref>		:'''Most any lab''': "Predictive analytics, for example, is one tool that the Pistoia Alliance is using to better understand laboratory instruments and how they might fail over time... With the right data management strategies and careful consideration of metadata, how to best store data so that it can be used in future AI and ML workflows is essential to the pursuit of AI in the laboratory. Utilizing technologies such as LIMS and ELN enables lab users to catalogue data, providing context and instrument parameters that can then be fed into AI or ML systems. Without the correct data or with mismatched data types, AI and ML will not be possible, or at the very least, could provide undue bias trying to compare data from disparate sources."<ref>{{Cite web \|date=29 January 2021 \|title=Data Analytics \|work=Scientific Computing World - Building a Smart Laboratory 2020 \|url=https://www.scientific-computing.com/feature/data-analytics-0 \|publisher=Europa Science Ltd \|accessdate=17 February 2023}}</ref>
	:'''Most any lab''': "When the actionable items are automatically created by Optima, the 'engine' starts working. An extremely sophisticated algorithm is able to assign the tasks to the resources, both laboratory personnel and instruments, according to the system configuration. Optima, thanks to a large amount of time dedicated to research the best way to automate this critical process, is able to automate most of the lab resource scheduling."<ref>{{Cite web \|last=Optima Team \|date=15 December 2020 \|title=The concept of machine learning applied to lab resources scheduling \|work=Optima Blog \|url=https://www.optima.life/blog/the-concept-of-machine-learning-applied-to-lab-resources-scheduling/ \|publisher=Optima PLC Tracking Tools S.L \|accessdate=17 February 2023}}</ref>		:'''Most any lab''': "When the actionable items are automatically created by Optima, the 'engine' starts working. An extremely sophisticated algorithm is able to assign the tasks to the resources, both laboratory personnel and instruments, according to the system configuration. Optima, thanks to a large amount of time dedicated to research the best way to automate this critical process, is able to automate most of the lab resource scheduling."<ref>{{Cite web \|last=Optima Team \|date=15 December 2020 \|title=The concept of machine learning applied to lab resources scheduling \|work=Optima Blog \|url=https://www.optima.life/blog/the-concept-of-machine-learning-applied-to-lab-resources-scheduling/ \|publisher=Optima PLC Tracking Tools S.L \|accessdate=17 February 2023}}</ref>

			*A number of challenges exist in the realm of effectively and securely implementing AI in the laboratory. This includes:

			:Ineffective AI implementations can challenge the underlying ethics of the work being done in the lab.<ref name=":10" />

	*Given those challenges, some considerations should be made about implementing AI-based components in the laboratory. Examples include:		*Given those challenges, some considerations should be made about implementing AI-based components in the laboratory. Examples include:
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	:'''Clinical diagnostics''': "From an industry and regulatory perspective, however, only the intended uses supported from the media manufacturer can be supported from AI applications, unless otherwise justified and substantive evidence is presented for additional claims support. This means strict adherence to specimen type and incubation conditions. Considering that the media was initially developed for human assessment using the well-trained microbiologist eye, and not an advanced imaging system with or without AI, this paradigm should shift to allow advancements in technology to challenge the status-quo of decreasing media read-times especially, as decreased read-times assist with laboratory turnaround times and thus patient management. Perhaps with an increasing body of evidence to support any proposed indications for use, either regulatory positions should be challenged, or manufacturers of media and industry AI-development specialists should work together to advance the field with new indications for use.		:'''Clinical diagnostics''': "From an industry and regulatory perspective, however, only the intended uses supported from the media manufacturer can be supported from AI applications, unless otherwise justified and substantive evidence is presented for additional claims support. This means strict adherence to specimen type and incubation conditions. Considering that the media was initially developed for human assessment using the well-trained microbiologist eye, and not an advanced imaging system with or without AI, this paradigm should shift to allow advancements in technology to challenge the status-quo of decreasing media read-times especially, as decreased read-times assist with laboratory turnaround times and thus patient management. Perhaps with an increasing body of evidence to support any proposed indications for use, either regulatory positions should be challenged, or manufacturers of media and industry AI-development specialists should work together to advance the field with new indications for use.
	:While the use of AI in the laboratory setting can be highly beneficial there are still some issues to be addressed. The first being phenotypically distinct single organism polymorphisms that may be interpreted by AI as separate organisms, as may also be the case for a human assessment, as well as small colony variant categorization. As detailed earlier, the broader the inputs, the greater the generalization of the model, and the higher the likelihood of algorithm accuracy. In that respect, understanding and planning around these design constraints is critical for ultimate deployment of algorithms. Additionally, expecting an AI system to correctly categorize “contamination” is a difficult task as often this again seemingly innocuous decision is dependent on years of experience and understanding the specimen type and the full clinical picture with detailed clinical histories. In this respect, a fully integrated AI-LIS system where all data is available may assist, but it is currently not possible to gather this granular detail needed to make this assessment reliable."<ref name=":7" />		:While the use of AI in the laboratory setting can be highly beneficial there are still some issues to be addressed. The first being phenotypically distinct single organism polymorphisms that may be interpreted by AI as separate organisms, as may also be the case for a human assessment, as well as small colony variant categorization. As detailed earlier, the broader the inputs, the greater the generalization of the model, and the higher the likelihood of algorithm accuracy. In that respect, understanding and planning around these design constraints is critical for ultimate deployment of algorithms. Additionally, expecting an AI system to correctly categorize “contamination” is a difficult task as often this again seemingly innocuous decision is dependent on years of experience and understanding the specimen type and the full clinical picture with detailed clinical histories. In this respect, a fully integrated AI-LIS system where all data is available may assist, but it is currently not possible to gather this granular detail needed to make this assessment reliable."<ref name=":7" />
	:'''Clinical diagnostics and pathology''': "Well, if I’ve learned anything in my research into this topic, it’s that AI implementation needs to be a two-way street. First, any company who is active in this space must reach out to pathologists and laboratory medicine professionals to understand their daily workflows, needs, and pain points in as much detail as possible. Second, pathologists, laboratory medicine professionals, and educators must all play their important part – willingly offering their time and expertise when it is sought or proactively getting involved. And finally, it’s clear that there is an imbalanced focus on certain issues – with privacy, respect, and sustainability falling by the wayside."<ref>{{Cite web \|last=Lee, G.F. \|date=10 October 2022 \|title=The Robot May See You Now: It’s time to stop and think about the ethics of artificial intelligence \|work=The Pathologist \|url=https://thepathologist.com/outside-the-lab/the-robot-may-see-you-now \|accessdate=17 February 2023}}</ref>		:'''Clinical diagnostics and pathology''': "Well, if I’ve learned anything in my research into this topic, it’s that AI implementation needs to be a two-way street. First, any company who is active in this space must reach out to pathologists and laboratory medicine professionals to understand their daily workflows, needs, and pain points in as much detail as possible. Second, pathologists, laboratory medicine professionals, and educators must all play their important part – willingly offering their time and expertise when it is sought or proactively getting involved. And finally, it’s clear that there is an imbalanced focus on certain issues – with privacy, respect, and sustainability falling by the wayside."<ref name=":10">{{Cite web \|last=Lee, G.F. \|date=10 October 2022 \|title=The Robot May See You Now: It’s time to stop and think about the ethics of artificial intelligence \|work=The Pathologist \|url=https://thepathologist.com/outside-the-lab/the-robot-may-see-you-now \|accessdate=17 February 2023}}</ref>
			:'''Most any lab''': A code of AI ethics should address objectivity, privacy, transparency, accountability, and sustainability in any AI implementation.<ref name=":10" />

	==References==		==References==
	{{Reflist\|colwidth=30em}}		{{Reflist\|colwidth=30em}}

Shawndouglas at 18:28, 17 February 2023

2023-02-17T18:28:46Z

Show changes

Shawndouglas at 00:16, 17 February 2023

2023-02-17T00:16:21Z

← Older revision		Revision as of 00:16, 17 February 2023
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	:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>		:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>
	:material testing labs<ref name=":2">{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref name=":3">{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>		:material testing labs<ref name=":2">{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref name=":3">{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>
	:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref>{{Cite journal \|last=Burger \|first=Benjamin \|last2=Maffettone \|first2=Phillip M. \|last3=Gusev \|first3=Vladimir V. \|last4=Aitchison \|first4=Catherine M. \|last5=Bai \|first5=Yang \|last6=Wang \|first6=Xiaoyan \|last7=Li \|first7=Xiaobo \|last8=Alston \|first8=Ben M. \|last9=Li \|first9=Buyi \|last10=Clowes \|first10=Rob \|last11=Rankin \|first11=Nicola \|date=2020-07-09 \|title=A mobile robotic chemist \|url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org \|journal=Nature \|language=en \|volume=583 \|issue=7815 \|pages=237–241 \|doi=10.1038/s41586-020-2442-2 \|issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal \|last=Lemonick, S. \|date=06 April 2020 \|title=Exploring chemical space: Can AI take us where no human has gone before? \|url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|journal=Chemical & Engineering News \|volume=98 \|issue=13 \|archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|archivedate=29 July 2020 \|accessdate=16 February 2023}}</ref>		:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref name=":4">{{Cite journal \|last=Burger \|first=Benjamin \|last2=Maffettone \|first2=Phillip M. \|last3=Gusev \|first3=Vladimir V. \|last4=Aitchison \|first4=Catherine M. \|last5=Bai \|first5=Yang \|last6=Wang \|first6=Xiaoyan \|last7=Li \|first7=Xiaobo \|last8=Alston \|first8=Ben M. \|last9=Li \|first9=Buyi \|last10=Clowes \|first10=Rob \|last11=Rankin \|first11=Nicola \|date=2020-07-09 \|title=A mobile robotic chemist \|url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org \|journal=Nature \|language=en \|volume=583 \|issue=7815 \|pages=237–241 \|doi=10.1038/s41586-020-2442-2 \|issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal \|last=Lemonick, S. \|date=06 April 2020 \|title=Exploring chemical space: Can AI take us where no human has gone before? \|url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|journal=Chemical & Engineering News \|volume=98 \|issue=13 \|archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|archivedate=29 July 2020 \|accessdate=16 February 2023}}</ref>
	:quantum physics labs<ref name="DoctrowArti19">{{cite web \|url=https://www.pnas.org/post/podcast/artificial-intelligence-laboratory \|title=Artificial intelligence in the laboratory \|author=Doctrow, B. \|work=PNAS Science Sessions \|date=16 December 2019 \|accessdate=16 February 2023}}</ref>		:quantum physics labs<ref name="DoctrowArti19">{{cite web \|url=https://www.pnas.org/post/podcast/artificial-intelligence-laboratory \|title=Artificial intelligence in the laboratory \|author=Doctrow, B. \|work=PNAS Science Sessions \|date=16 December 2019 \|accessdate=16 February 2023}}</ref>

	*What's going on in these labs?		*What's going on in these labs?

	:Materials ~~lab~~: The creation of "a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions ..."<ref name=":2" />		:Materials science: The creation of "a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions ..."<ref name=":2" />
	:Materials ~~lab~~: "Most of the applications of [machine learning (ML)] in chemical and materials sciences, as we have said, feature supervised learning algorithms. The goal there is to supplement or replace traditional modeling methods, at the quantum chemical or classical level, in order to predict the properties of molecules or materials directly from their structure or their chemical composition ... Our research group was applying the same idea on a narrower range of materials, trying to confirm that for a given chemical composition, geometrical descriptors of a material’s structure could lead to accurate predictions of its mechanical features."<ref name=":3" />		:Materials science: "Most of the applications of [machine learning (ML)] in chemical and materials sciences, as we have said, feature supervised learning algorithms. The goal there is to supplement or replace traditional modeling methods, at the quantum chemical or classical level, in order to predict the properties of molecules or materials directly from their structure or their chemical composition ... Our research group was applying the same idea on a narrower range of materials, trying to confirm that for a given chemical composition, geometrical descriptors of a material’s structure could lead to accurate predictions of its mechanical features."<ref name=":3" />
			:Chemistry and molecular science: "The benefits of combining automated experimentation with a layer of artificial intelligence (AI) have been demonstrated for flow reactors, photovoltaic films, organic synthesis, perovskites and in formulation problems. However, so far no approaches have integrated mobile robotics with AI for chemical experiments. Here, we built Bayesian optimization into a mobile robotic workflow to conduct photocatalysis experiments within a ten-dimensional space."<ref name=":4" />
			:Clinical research: "... retrospective analysis of existing patient data for descriptive and clustering purposes [and] automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses ..."<ref name=":0" />
			:Clinical research: "AI ... offers a further layer to the laboratory system by analyzing all experimental data collected by experiment devices, whether it be a sensor or a collaborative robot. From data collected, AI is able to produce hypotheses and predict which combination of materials or temperature is desired for the experiment. In short, this system will allow scientists to be aided by a highly intelligent system which is constantly monitoring and analyzing the experimental output. In this way, AI will help an experiment from its inception to conclusion."<ref>{{Cite web \|last=Chubb, P. \|date=03 November 2020 \|title=How disruptive technology is helping laboratories combat COVID-19 \|url=https://datafloq.com/read/disruptive-technologies-lab-help-us-prepare-future-pandemics/ \|publisher=Datafloq \|accessdate=16 February 2023}}</ref>
			:Medical diagnostics: Development of [[Clinical decision support system\|clinical decision support systems]] <ref name=":0" /><ref name=":1" />

	==References==		==References==
	{{Reflist\|colwidth=30em}}		{{Reflist\|colwidth=30em}}

Shawndouglas at 23:54, 16 February 2023

2023-02-16T23:54:50Z

← Older revision		Revision as of 23:54, 16 February 2023
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	:reproduction clinics<ref>{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref><ref name="ESHREArti22">{{cite web \|url=https://www.focusonreproduction.eu/article/ESHRE-News-22AI \|title=Annual Meeting 2022: Artificial intelligence in embryology and ART \|author=European Society of Human Reproduction and Embryology \|work=Focus on Reproduction \|date=06 July 2022 \|accessdate=16 February 2023}}</ref><ref name="HinckleyApply21">{{cite web \|url=https://rscbayarea.com/blog/applying-ai-for-better-ivf-success \|title=Applying AI (Artificial Intelligence) in the Lab for Better IVF Success \|author=Hinckley, M. \|work=Reproductive Science Center Blog \|publisher=Reproductive Science Center of the Bay Area \|date=17 March 2021 \|accessdate=16 February 2023}}</ref>		:reproduction clinics<ref>{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref><ref name="ESHREArti22">{{cite web \|url=https://www.focusonreproduction.eu/article/ESHRE-News-22AI \|title=Annual Meeting 2022: Artificial intelligence in embryology and ART \|author=European Society of Human Reproduction and Embryology \|work=Focus on Reproduction \|date=06 July 2022 \|accessdate=16 February 2023}}</ref><ref name="HinckleyApply21">{{cite web \|url=https://rscbayarea.com/blog/applying-ai-for-better-ivf-success \|title=Applying AI (Artificial Intelligence) in the Lab for Better IVF Success \|author=Hinckley, M. \|work=Reproductive Science Center Blog \|publisher=Reproductive Science Center of the Bay Area \|date=17 March 2021 \|accessdate=16 February 2023}}</ref>
	:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>		:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>
	:material testing labs<ref>{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref>{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>		:material testing labs<ref name=":2">{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref name=":3">{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>
	:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref>{{Cite journal \|last=Burger \|first=Benjamin \|last2=Maffettone \|first2=Phillip M. \|last3=Gusev \|first3=Vladimir V. \|last4=Aitchison \|first4=Catherine M. \|last5=Bai \|first5=Yang \|last6=Wang \|first6=Xiaoyan \|last7=Li \|first7=Xiaobo \|last8=Alston \|first8=Ben M. \|last9=Li \|first9=Buyi \|last10=Clowes \|first10=Rob \|last11=Rankin \|first11=Nicola \|date=2020-07-09 \|title=A mobile robotic chemist \|url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org \|journal=Nature \|language=en \|volume=583 \|issue=7815 \|pages=237–241 \|doi=10.1038/s41586-020-2442-2 \|issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal \|last=Lemonick, S. \|date=06 April 2020 \|title=Exploring chemical space: Can AI take us where no human has gone before? \|url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|journal=Chemical & Engineering News \|volume=98 \|issue=13 \|archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|archivedate=29 July 2020 \|accessdate=16 February 2023}}</ref>		:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref>{{Cite journal \|last=Burger \|first=Benjamin \|last2=Maffettone \|first2=Phillip M. \|last3=Gusev \|first3=Vladimir V. \|last4=Aitchison \|first4=Catherine M. \|last5=Bai \|first5=Yang \|last6=Wang \|first6=Xiaoyan \|last7=Li \|first7=Xiaobo \|last8=Alston \|first8=Ben M. \|last9=Li \|first9=Buyi \|last10=Clowes \|first10=Rob \|last11=Rankin \|first11=Nicola \|date=2020-07-09 \|title=A mobile robotic chemist \|url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org \|journal=Nature \|language=en \|volume=583 \|issue=7815 \|pages=237–241 \|doi=10.1038/s41586-020-2442-2 \|issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal \|last=Lemonick, S. \|date=06 April 2020 \|title=Exploring chemical space: Can AI take us where no human has gone before? \|url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|journal=Chemical & Engineering News \|volume=98 \|issue=13 \|archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|archivedate=29 July 2020 \|accessdate=16 February 2023}}</ref>
	:quantum physics labs<ref name="DoctrowArti19">{{cite web \|url=https://www.pnas.org/post/podcast/artificial-intelligence-laboratory \|title=Artificial intelligence in the laboratory \|author=Doctrow, B. \|work=PNAS Science Sessions \|date=16 December 2019 \|accessdate=16 February 2023}}</ref>		:quantum physics labs<ref name="DoctrowArti19">{{cite web \|url=https://www.pnas.org/post/podcast/artificial-intelligence-laboratory \|title=Artificial intelligence in the laboratory \|author=Doctrow, B. \|work=PNAS Science Sessions \|date=16 December 2019 \|accessdate=16 February 2023}}</ref>

			*What's going on in these labs?

			:Materials lab: The creation of "a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions ..."<ref name=":2" />
			:Materials lab: "Most of the applications of [machine learning (ML)] in chemical and materials sciences, as we have said, feature supervised learning algorithms. The goal there is to supplement or replace traditional modeling methods, at the quantum chemical or classical level, in order to predict the properties of molecules or materials directly from their structure or their chemical composition ... Our research group was applying the same idea on a narrower range of materials, trying to confirm that for a given chemical composition, geometrical descriptors of a material’s structure could lead to accurate predictions of its mechanical features."<ref name=":3" />

	==References==		==References==
	{{Reflist\|colwidth=30em}}		{{Reflist\|colwidth=30em}}

Shawndouglas at 23:39, 16 February 2023

2023-02-16T23:39:15Z

← Older revision		Revision as of 23:39, 16 February 2023
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	*Discussion and practical use of [[artificial intelligence]] (AI) in the [[laboratory]] is, perhaps to the surprise of some, not a recent phenomena. In the mid-1980s, researchers were developing computerized AI systems able "to develop automatic decision rules for follow-up analysis of [[[clinical laboratory]]] tests depending on prior information, thus avoiding the delays of traditional sequential testing and the costs of unnecessary parallel testing."<ref>{{Cite journal \|last=Berger-Hershkowitz \|first=H. \|last2=Neuhauser \|first2=D. \|date=1987 \|title=Artificial intelligence in the clinical laboratory \|url=https://www.ccjm.org/content/54/3/165 \|journal=Cleveland Clinic Journal of Medicine \|volume=54 \|issue=3 \|pages=165–166 \|doi=10.3949/ccjm.54.3.165 \|issn=0891-1150 \|pmid=3301059}}</ref> In fact, discussion of AI in general was ongoing even in the mid-1950s.<ref name="MinskyHeuristic56">{{cite book \|url=https://books.google.com/books?hl=en&lr=&id=fvWNo6_IZGUC&oi=fnd&pg=PA1 \|title=Heuristic Aspects of the Artificial Intelligence Problem \|author=Minsky, M. \|publisher=Ed Services Technical Information Agency \|date=17 December 1956 \|accessdate=16 February 2023}}</ref><ref>{{Cite journal \|last=Minsky \|first=Marvin \|date=1961-01 \|title=Steps toward Artificial Intelligence \|url=http://ieeexplore.ieee.org/document/4066245/ \|journal=Proceedings of the IRE \|volume=49 \|issue=1 \|pages=8–30 \|doi=10.1109/JRPROC.1961.287775 \|issn=0096-8390}}</ref>		*Discussion and practical use of [[artificial intelligence]] (AI) in the [[laboratory]] is, perhaps to the surprise of some, not a recent phenomena. In the mid-1980s, researchers were developing computerized AI systems able "to develop automatic decision rules for follow-up analysis of [[[clinical laboratory]]] tests depending on prior information, thus avoiding the delays of traditional sequential testing and the costs of unnecessary parallel testing."<ref>{{Cite journal \|last=Berger-Hershkowitz \|first=H. \|last2=Neuhauser \|first2=D. \|date=1987 \|title=Artificial intelligence in the clinical laboratory \|url=https://www.ccjm.org/content/54/3/165 \|journal=Cleveland Clinic Journal of Medicine \|volume=54 \|issue=3 \|pages=165–166 \|doi=10.3949/ccjm.54.3.165 \|issn=0891-1150 \|pmid=3301059}}</ref> In fact, discussion of AI in general was ongoing even in the mid-1950s.<ref name="MinskyHeuristic56">{{cite book \|url=https://books.google.com/books?hl=en&lr=&id=fvWNo6_IZGUC&oi=fnd&pg=PA1 \|title=Heuristic Aspects of the Artificial Intelligence Problem \|author=Minsky, M. \|publisher=Ed Services Technical Information Agency \|date=17 December 1956 \|accessdate=16 February 2023}}</ref><ref>{{Cite journal \|last=Minsky \|first=Marvin \|date=1961-01 \|title=Steps toward Artificial Intelligence \|url=http://ieeexplore.ieee.org/document/4066245/ \|journal=Proceedings of the IRE \|volume=49 \|issue=1 \|pages=8–30 \|doi=10.1109/JRPROC.1961.287775 \|issn=0096-8390}}</ref>

	*Today, AI is practically ~~being~~ used in not only clinical laboratories but also clinical research labs, ~~as well as~~ life science and research and development (R&D) labs. Practical uses of AI can be found in:		*Today, AI is being practically used in not only clinical diagnostic laboratories but also clinical research labs, life science labs, and research and development (R&D) labs, and more. Practical uses of AI can be found in:

	:clinical research labs<ref name=":0">{{Cite journal \|last=Damiani \|first=A. \|last2=Masciocchi \|first2=C. \|last3=Lenkowicz \|first3=J. \|last4=Capocchiano \|first4=N. D. \|last5=Boldrini \|first5=L. \|last6=Tagliaferri \|first6=L. \|last7=Cesario \|first7=A. \|last8=Sergi \|first8=P. \|last9=Marchetti \|first9=A. \|last10=Luraschi \|first10=A. \|last11=Patarnello \|first11=S. \|date=2021-12-07 \|title=Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator \|url=https://www.frontiersin.org/articles/10.3389/fcomp.2021.768266/full \|journal=Frontiers in Computer Science \|volume=3 \|pages=768266 \|doi=10.3389/fcomp.2021.768266 \|issn=2624-9898}}</ref>		:clinical research labs<ref name=":0">{{Cite journal \|last=Damiani \|first=A. \|last2=Masciocchi \|first2=C. \|last3=Lenkowicz \|first3=J. \|last4=Capocchiano \|first4=N. D. \|last5=Boldrini \|first5=L. \|last6=Tagliaferri \|first6=L. \|last7=Cesario \|first7=A. \|last8=Sergi \|first8=P. \|last9=Marchetti \|first9=A. \|last10=Luraschi \|first10=A. \|last11=Patarnello \|first11=S. \|date=2021-12-07 \|title=Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator \|url=https://www.frontiersin.org/articles/10.3389/fcomp.2021.768266/full \|journal=Frontiers in Computer Science \|volume=3 \|pages=768266 \|doi=10.3389/fcomp.2021.768266 \|issn=2624-9898}}</ref>

Shawndouglas at 23:38, 16 February 2023

2023-02-16T23:38:05Z

← Older revision		Revision as of 23:38, 16 February 2023
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	:clinical research labs<ref name=":0">{{Cite journal \|last=Damiani \|first=A. \|last2=Masciocchi \|first2=C. \|last3=Lenkowicz \|first3=J. \|last4=Capocchiano \|first4=N. D. \|last5=Boldrini \|first5=L. \|last6=Tagliaferri \|first6=L. \|last7=Cesario \|first7=A. \|last8=Sergi \|first8=P. \|last9=Marchetti \|first9=A. \|last10=Luraschi \|first10=A. \|last11=Patarnello \|first11=S. \|date=2021-12-07 \|title=Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator \|url=https://www.frontiersin.org/articles/10.3389/fcomp.2021.768266/full \|journal=Frontiers in Computer Science \|volume=3 \|pages=768266 \|doi=10.3389/fcomp.2021.768266 \|issn=2624-9898}}</ref>		:clinical research labs<ref name=":0">{{Cite journal \|last=Damiani \|first=A. \|last2=Masciocchi \|first2=C. \|last3=Lenkowicz \|first3=J. \|last4=Capocchiano \|first4=N. D. \|last5=Boldrini \|first5=L. \|last6=Tagliaferri \|first6=L. \|last7=Cesario \|first7=A. \|last8=Sergi \|first8=P. \|last9=Marchetti \|first9=A. \|last10=Luraschi \|first10=A. \|last11=Patarnello \|first11=S. \|date=2021-12-07 \|title=Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator \|url=https://www.frontiersin.org/articles/10.3389/fcomp.2021.768266/full \|journal=Frontiers in Computer Science \|volume=3 \|pages=768266 \|doi=10.3389/fcomp.2021.768266 \|issn=2624-9898}}</ref>
	:hospitals<ref name=":0" /><ref name=":1">{{Cite journal \|last=University of California, San Francisco \|last2=Adler-Milstein \|first2=Julia \|last3=Aggarwal \|first3=Nakul \|last4=University of Wisconsin-Madison \|last5=Ahmed \|first5=Mahnoor \|last6=National Academy of Medicine \|last7=Castner \|first7=Jessica \|last8=Castner Incorporated \|last9=Evans \|first9=Barbara J. \|last10=University of Florida \|last11=Gonzalez \|first11=Andrew A. \|date=2022-09-29 \|title=Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis \|url=https://nam.edu/meeting-the-moment-addressing-barriers-and-facilitating-clinical-adoption-of-artificial-intelligence-in-medical-diagnosis \|journal=NAM Perspectives \|volume=22 \|issue=9 \|doi=10.31478/202209c \|pmc=PMC9875857 \|pmid=36713769}}</ref>		:hospitals<ref name=":0" /><ref name=":1">{{Cite journal \|last=University of California, San Francisco \|last2=Adler-Milstein \|first2=Julia \|last3=Aggarwal \|first3=Nakul \|last4=University of Wisconsin-Madison \|last5=Ahmed \|first5=Mahnoor \|last6=National Academy of Medicine \|last7=Castner \|first7=Jessica \|last8=Castner Incorporated \|last9=Evans \|first9=Barbara J. \|last10=University of Florida \|last11=Gonzalez \|first11=Andrew A. \|date=2022-09-29 \|title=Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis \|url=https://nam.edu/meeting-the-moment-addressing-barriers-and-facilitating-clinical-adoption-of-artificial-intelligence-in-medical-diagnosis \|journal=NAM Perspectives \|volume=22 \|issue=9 \|doi=10.31478/202209c \|pmc=PMC9875857 \|pmid=36713769}}</ref>
	:medical diagnostics labs<ref name=":1" /><ref>{{Cite web \|last=Government Accountability Office (GAO); National Academy of Medicine (NAM) \|date=September 2022 \|title=Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics \|url=https://www.gao.gov/assets/gao-22-104629.pdf \|format=PDF \|publisher=Government Accountability Office \|accessdate=16 February 2023}}</ref><ref>{{Cite journal \|last=Wen \|first=Xiaoxia \|last2=Leng \|first2=Ping \|last3=Wang \|first3=Jiasi \|last4=Yang \|first4=Guishu \|last5=Zu \|first5=Ruiling \|last6=Jia \|first6=Xiaojiong \|last7=Zhang \|first7=Kaijiong \|last8=Mengesha \|first8=Birga Anteneh \|last9=Huang \|first9=Jian \|last10=Wang \|first10=Dongsheng \|last11=Luo \|first11=Huaichao \|date=2022-09-24 \|title=Clinlabomics: leveraging clinical laboratory data by data mining strategies \|url=https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04926-1 \|journal=BMC Bioinformatics \|language=en \|volume=23 \|issue=1 \|pages=387 \|doi=10.1186/s12859-022-04926-1 \|issn=1471-2105 \|pmc=PMC9509545 \|pmid=36153474}}</ref><ref>{{Cite journal \|last=DeYoung \|first=B. \|last2=Morales \|first2=M. \|last3=Giglio \|first3=S. \|date=2022-08-04 \|title=Microbiology 2.0–A “behind the scenes” consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories \|url=https://www.frontiersin.org/articles/10.3389/fmicb.2022.976068/full \|journal=Frontiers in Microbiology \|volume=13 \|pages=976068 \|doi=10.3389/fmicb.2022.976068 \|issn=1664-302X \|pmc=PMC9386241 \|pmid=35992715}}</ref><ref>{{Cite web \|last=Schut, M. \|date=01 December 2022 \|title=Get better with bytes \|url=https://www.amsterdamumc.org/en/research/news/get-better-with-bytes.htm \|publisher=Amsterdam UMC \|accessdate=16 February 2023}}</ref>		:medical diagnostics labs<ref name=":1" /><ref>{{Cite web \|last=Government Accountability Office (GAO); National Academy of Medicine (NAM) \|date=September 2022 \|title=Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics \|url=https://www.gao.gov/assets/gao-22-104629.pdf \|format=PDF \|publisher=Government Accountability Office \|accessdate=16 February 2023}}</ref><ref>{{Cite journal \|last=Wen \|first=Xiaoxia \|last2=Leng \|first2=Ping \|last3=Wang \|first3=Jiasi \|last4=Yang \|first4=Guishu \|last5=Zu \|first5=Ruiling \|last6=Jia \|first6=Xiaojiong \|last7=Zhang \|first7=Kaijiong \|last8=Mengesha \|first8=Birga Anteneh \|last9=Huang \|first9=Jian \|last10=Wang \|first10=Dongsheng \|last11=Luo \|first11=Huaichao \|date=2022-09-24 \|title=Clinlabomics: leveraging clinical laboratory data by data mining strategies \|url=https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04926-1 \|journal=BMC Bioinformatics \|language=en \|volume=23 \|issue=1 \|pages=387 \|doi=10.1186/s12859-022-04926-1 \|issn=1471-2105 \|pmc=PMC9509545 \|pmid=36153474}}</ref><ref>{{Cite journal \|last=DeYoung \|first=B. \|last2=Morales \|first2=M. \|last3=Giglio \|first3=S. \|date=2022-08-04 \|title=Microbiology 2.0–A “behind the scenes” consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories \|url=https://www.frontiersin.org/articles/10.3389/fmicb.2022.976068/full \|journal=Frontiers in Microbiology \|volume=13 \|pages=976068 \|doi=10.3389/fmicb.2022.976068 \|issn=1664-302X \|pmc=PMC9386241 \|pmid=35992715}}</ref><ref>{{Cite web \|last=Schut, M. \|date=01 December 2022 \|title=Get better with bytes \|url=https://www.amsterdamumc.org/en/research/news/get-better-with-bytes.htm \|publisher=Amsterdam UMC \|accessdate=16 February 2023}}</ref><ref name="AlbanoCal19">{{cite web \|url=https://physicianslab.com/calculations-to-diagnosis-the-artificial-intelligence-shift-thats-already-happening/ \|title=Calculations to Diagnosis: The Artificial Intelligence Shift That’s Already Happening \|author=Albano, V.; Morris, C.; Kent, T. \|work=Physicians Lab \|date=06 December 2019 \|accessdate=16 February 2023}}</ref>
			:chromatography labs<ref name="AlbanoCal19" />
	:biology and life science labs<ref>{{Cite journal \|last=de Ridder \|first=Dick \|date=2019-01 \|title=Artificial intelligence in the lab: ask not what your computer can do for you \|url=https://onlinelibrary.wiley.com/doi/10.1111/1751-7915.13317 \|journal=Microbial Biotechnology \|language=en \|volume=12 \|issue=1 \|pages=38–40 \|doi=10.1111/1751-7915.13317 \|pmc=PMC6302702 \|pmid=30246499}}</ref>		:biology and life science labs<ref>{{Cite journal \|last=de Ridder \|first=Dick \|date=2019-01 \|title=Artificial intelligence in the lab: ask not what your computer can do for you \|url=https://onlinelibrary.wiley.com/doi/10.1111/1751-7915.13317 \|journal=Microbial Biotechnology \|language=en \|volume=12 \|issue=1 \|pages=38–40 \|doi=10.1111/1751-7915.13317 \|pmc=PMC6302702 \|pmid=30246499}}</ref>
	:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web \|url=https://siim.org/page/22w_clinical_adoption_of_ai \|title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties \|author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. \|publisher=Society for Imaging Informatics in Medicine \|date=11 October 2022}}</ref>		:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web \|url=https://siim.org/page/22w_clinical_adoption_of_ai \|title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties \|author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. \|publisher=Society for Imaging Informatics in Medicine \|date=11 October 2022}}</ref>
	:ophthalmology clinics<ref>{{Cite journal \|last=He \|first=Mingguang \|last2=Li \|first2=Zhixi \|last3=Liu \|first3=Chi \|last4=Shi \|first4=Danli \|last5=Tan \|first5=Zachary \|date=2020-07 \|title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge \|url=https://journals.lww.com/10.1097/APO.0000000000000301 \|journal=Asia-Pacific Journal of Ophthalmology \|language=en \|volume=9 \|issue=4 \|pages=299–307 \|doi=10.1097/APO.0000000000000301 \|issn=2162-0989}}</ref>		:ophthalmology clinics<ref>{{Cite journal \|last=He \|first=Mingguang \|last2=Li \|first2=Zhixi \|last3=Liu \|first3=Chi \|last4=Shi \|first4=Danli \|last5=Tan \|first5=Zachary \|date=2020-07 \|title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge \|url=https://journals.lww.com/10.1097/APO.0000000000000301 \|journal=Asia-Pacific Journal of Ophthalmology \|language=en \|volume=9 \|issue=4 \|pages=299–307 \|doi=10.1097/APO.0000000000000301 \|issn=2162-0989}}</ref>
	:reproduction clinics<ref>{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref>		:reproduction clinics<ref>{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref><ref name="ESHREArti22">{{cite web \|url=https://www.focusonreproduction.eu/article/ESHRE-News-22AI \|title=Annual Meeting 2022: Artificial intelligence in embryology and ART \|author=European Society of Human Reproduction and Embryology \|work=Focus on Reproduction \|date=06 July 2022 \|accessdate=16 February 2023}}</ref><ref name="HinckleyApply21">{{cite web \|url=https://rscbayarea.com/blog/applying-ai-for-better-ivf-success \|title=Applying AI (Artificial Intelligence) in the Lab for Better IVF Success \|author=Hinckley, M. \|work=Reproductive Science Center Blog \|publisher=Reproductive Science Center of the Bay Area \|date=17 March 2021 \|accessdate=16 February 2023}}</ref>
	:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>		:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>
	:material testing labs<ref>{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref>{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>		:material testing labs<ref>{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref>{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>

Shawndouglas at 17:35, 16 February 2023

2023-02-16T17:35:46Z

← Older revision		Revision as of 17:35, 16 February 2023
Line 12:		Line 12:
	*Today, AI is practically being used in not only clinical laboratories but also clinical research labs, as well as life science and research and development (R&D) labs. Practical uses of AI can be found in:		*Today, AI is practically being used in not only clinical laboratories but also clinical research labs, as well as life science and research and development (R&D) labs. Practical uses of AI can be found in:

			:clinical research labs<ref name=":0">{{Cite journal \|last=Damiani \|first=A. \|last2=Masciocchi \|first2=C. \|last3=Lenkowicz \|first3=J. \|last4=Capocchiano \|first4=N. D. \|last5=Boldrini \|first5=L. \|last6=Tagliaferri \|first6=L. \|last7=Cesario \|first7=A. \|last8=Sergi \|first8=P. \|last9=Marchetti \|first9=A. \|last10=Luraschi \|first10=A. \|last11=Patarnello \|first11=S. \|date=2021-12-07 \|title=Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator \|url=https://www.frontiersin.org/articles/10.3389/fcomp.2021.768266/full \|journal=Frontiers in Computer Science \|volume=3 \|pages=768266 \|doi=10.3389/fcomp.2021.768266 \|issn=2624-9898}}</ref>
			:hospitals<ref name=":0" /><ref name=":1">{{Cite journal \|last=University of California, San Francisco \|last2=Adler-Milstein \|first2=Julia \|last3=Aggarwal \|first3=Nakul \|last4=University of Wisconsin-Madison \|last5=Ahmed \|first5=Mahnoor \|last6=National Academy of Medicine \|last7=Castner \|first7=Jessica \|last8=Castner Incorporated \|last9=Evans \|first9=Barbara J. \|last10=University of Florida \|last11=Gonzalez \|first11=Andrew A. \|date=2022-09-29 \|title=Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis \|url=https://nam.edu/meeting-the-moment-addressing-barriers-and-facilitating-clinical-adoption-of-artificial-intelligence-in-medical-diagnosis \|journal=NAM Perspectives \|volume=22 \|issue=9 \|doi=10.31478/202209c \|pmc=PMC9875857 \|pmid=36713769}}</ref>
			:medical diagnostics labs<ref name=":1" /><ref>{{Cite web \|last=Government Accountability Office (GAO); National Academy of Medicine (NAM) \|date=September 2022 \|title=Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics \|url=https://www.gao.gov/assets/gao-22-104629.pdf \|format=PDF \|publisher=Government Accountability Office \|accessdate=16 February 2023}}</ref><ref>{{Cite journal \|last=Wen \|first=Xiaoxia \|last2=Leng \|first2=Ping \|last3=Wang \|first3=Jiasi \|last4=Yang \|first4=Guishu \|last5=Zu \|first5=Ruiling \|last6=Jia \|first6=Xiaojiong \|last7=Zhang \|first7=Kaijiong \|last8=Mengesha \|first8=Birga Anteneh \|last9=Huang \|first9=Jian \|last10=Wang \|first10=Dongsheng \|last11=Luo \|first11=Huaichao \|date=2022-09-24 \|title=Clinlabomics: leveraging clinical laboratory data by data mining strategies \|url=https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04926-1 \|journal=BMC Bioinformatics \|language=en \|volume=23 \|issue=1 \|pages=387 \|doi=10.1186/s12859-022-04926-1 \|issn=1471-2105 \|pmc=PMC9509545 \|pmid=36153474}}</ref><ref>{{Cite journal \|last=DeYoung \|first=B. \|last2=Morales \|first2=M. \|last3=Giglio \|first3=S. \|date=2022-08-04 \|title=Microbiology 2.0–A “behind the scenes” consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories \|url=https://www.frontiersin.org/articles/10.3389/fmicb.2022.976068/full \|journal=Frontiers in Microbiology \|volume=13 \|pages=976068 \|doi=10.3389/fmicb.2022.976068 \|issn=1664-302X \|pmc=PMC9386241 \|pmid=35992715}}</ref><ref>{{Cite web \|last=Schut, M. \|date=01 December 2022 \|title=Get better with bytes \|url=https://www.amsterdamumc.org/en/research/news/get-better-with-bytes.htm \|publisher=Amsterdam UMC \|accessdate=16 February 2023}}</ref>
			:biology and life science labs<ref>{{Cite journal \|last=de Ridder \|first=Dick \|date=2019-01 \|title=Artificial intelligence in the lab: ask not what your computer can do for you \|url=https://onlinelibrary.wiley.com/doi/10.1111/1751-7915.13317 \|journal=Microbial Biotechnology \|language=en \|volume=12 \|issue=1 \|pages=38–40 \|doi=10.1111/1751-7915.13317 \|pmc=PMC6302702 \|pmid=30246499}}</ref>
	:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web \|url=https://siim.org/page/22w_clinical_adoption_of_ai \|title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties \|author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. \|publisher=Society for Imaging Informatics in Medicine \|date=11 October 2022}}</ref>		:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web \|url=https://siim.org/page/22w_clinical_adoption_of_ai \|title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties \|author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. \|publisher=Society for Imaging Informatics in Medicine \|date=11 October 2022}}</ref>
	:ophthalmology clinics<ref>{{Cite journal \|last=He \|first=Mingguang \|last2=Li \|first2=Zhixi \|last3=Liu \|first3=Chi \|last4=Shi \|first4=Danli \|last5=Tan \|first5=Zachary \|date=2020-07 \|title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge \|url=https://journals.lww.com/10.1097/APO.0000000000000301 \|journal=Asia-Pacific Journal of Ophthalmology \|language=en \|volume=9 \|issue=4 \|pages=299–307 \|doi=10.1097/APO.0000000000000301 \|issn=2162-0989}}</ref>		:ophthalmology clinics<ref>{{Cite journal \|last=He \|first=Mingguang \|last2=Li \|first2=Zhixi \|last3=Liu \|first3=Chi \|last4=Shi \|first4=Danli \|last5=Tan \|first5=Zachary \|date=2020-07 \|title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge \|url=https://journals.lww.com/10.1097/APO.0000000000000301 \|journal=Asia-Pacific Journal of Ophthalmology \|language=en \|volume=9 \|issue=4 \|pages=299–307 \|doi=10.1097/APO.0000000000000301 \|issn=2162-0989}}</ref>
			:reproduction clinics<ref>{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref>
	:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>		:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>
	:material testing labs<ref>{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref>{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>		:material testing labs<ref>{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref>{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>
	:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref>{{Cite journal \|last=Burger \|first=Benjamin \|last2=Maffettone \|first2=Phillip M. \|last3=Gusev \|first3=Vladimir V. \|last4=Aitchison \|first4=Catherine M. \|last5=Bai \|first5=Yang \|last6=Wang \|first6=Xiaoyan \|last7=Li \|first7=Xiaobo \|last8=Alston \|first8=Ben M. \|last9=Li \|first9=Buyi \|last10=Clowes \|first10=Rob \|last11=Rankin \|first11=Nicola \|date=2020-07-09 \|title=A mobile robotic chemist \|url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org \|journal=Nature \|language=en \|volume=583 \|issue=7815 \|pages=237–241 \|doi=10.1038/s41586-020-2442-2 \|issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal \|last=Lemonick, S. \|date=06 April 2020 \|title=Exploring chemical space: Can AI take us where no human has gone before? \|url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|journal=Chemical & Engineering News \|volume=98 \|issue=13 \|archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|archivedate=29 July 2020 \|accessdate=16 February 2023}}</ref>		:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref>{{Cite journal \|last=Burger \|first=Benjamin \|last2=Maffettone \|first2=Phillip M. \|last3=Gusev \|first3=Vladimir V. \|last4=Aitchison \|first4=Catherine M. \|last5=Bai \|first5=Yang \|last6=Wang \|first6=Xiaoyan \|last7=Li \|first7=Xiaobo \|last8=Alston \|first8=Ben M. \|last9=Li \|first9=Buyi \|last10=Clowes \|first10=Rob \|last11=Rankin \|first11=Nicola \|date=2020-07-09 \|title=A mobile robotic chemist \|url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org \|journal=Nature \|language=en \|volume=583 \|issue=7815 \|pages=237–241 \|doi=10.1038/s41586-020-2442-2 \|issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal \|last=Lemonick, S. \|date=06 April 2020 \|title=Exploring chemical space: Can AI take us where no human has gone before? \|url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|journal=Chemical & Engineering News \|volume=98 \|issue=13 \|archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|archivedate=29 July 2020 \|accessdate=16 February 2023}}</ref>
			:quantum physics labs<ref name="DoctrowArti19">{{cite web \|url=https://www.pnas.org/post/podcast/artificial-intelligence-laboratory \|title=Artificial intelligence in the laboratory \|author=Doctrow, B. \|work=PNAS Science Sessions \|date=16 December 2019 \|accessdate=16 February 2023}}</ref>


	==References==		==References==
	{{Reflist\|colwidth=30em}}		{{Reflist\|colwidth=30em}}

Shawndouglas at 17:04, 16 February 2023

2023-02-16T17:04:46Z

← Older revision		Revision as of 17:04, 16 February 2023
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	==Sandbox begins below==		==Sandbox begins below==

	Discussion and practical use of [[artificial intelligence]] (AI) in the [[laboratory]] is, perhaps to the surprise of some, not a recent phenomena. In the mid-1980s, researchers were developing computerized AI systems able "to develop automatic decision rules for follow-up analysis of [laboratory] tests depending on prior information, thus avoiding the delays of traditional sequential testing and the costs of unnecessary parallel testing."<ref>{{Cite journal \|last=Berger-Hershkowitz \|first=H. \|last2=Neuhauser \|first2=D. \|date=1987 \|title=Artificial intelligence in the clinical laboratory \|url=https://www.ccjm.org/content/54/3/165 \|journal=Cleveland Clinic Journal of Medicine \|volume=54 \|issue=3 \|pages=165–166 \|doi=10.3949/ccjm.54.3.165 \|issn=0891-1150 \|pmid=3301059}}</ref> In fact, discussion of AI in general was ongoing even in the mid-1950s.<ref name="MinskyHeuristic56">{{cite book \|url=https://books.google.com/books?hl=en&lr=&id=fvWNo6_IZGUC&oi=fnd&pg=PA1 \|title=Heuristic Aspects of the Artificial Intelligence Problem \|author=Minsky, M. \|publisher=Ed Services Technical Information Agency \|date=17 December 1956 \|accessdate=16 February 2023}}</ref><ref>{{Cite journal \|last=Minsky \|first=Marvin \|date=1961-01 \|title=Steps toward Artificial Intelligence \|url=http://ieeexplore.ieee.org/document/4066245/ \|journal=Proceedings of the IRE \|volume=49 \|issue=1 \|pages=8–30 \|doi=10.1109/JRPROC.1961.287775 \|issn=0096-8390}}</ref>		*Discussion and practical use of [[artificial intelligence]] (AI) in the [[laboratory]] is, perhaps to the surprise of some, not a recent phenomena. In the mid-1980s, researchers were developing computerized AI systems able "to develop automatic decision rules for follow-up analysis of [[[clinical laboratory]]] tests depending on prior information, thus avoiding the delays of traditional sequential testing and the costs of unnecessary parallel testing."<ref>{{Cite journal \|last=Berger-Hershkowitz \|first=H. \|last2=Neuhauser \|first2=D. \|date=1987 \|title=Artificial intelligence in the clinical laboratory \|url=https://www.ccjm.org/content/54/3/165 \|journal=Cleveland Clinic Journal of Medicine \|volume=54 \|issue=3 \|pages=165–166 \|doi=10.3949/ccjm.54.3.165 \|issn=0891-1150 \|pmid=3301059}}</ref> In fact, discussion of AI in general was ongoing even in the mid-1950s.<ref name="MinskyHeuristic56">{{cite book \|url=https://books.google.com/books?hl=en&lr=&id=fvWNo6_IZGUC&oi=fnd&pg=PA1 \|title=Heuristic Aspects of the Artificial Intelligence Problem \|author=Minsky, M. \|publisher=Ed Services Technical Information Agency \|date=17 December 1956 \|accessdate=16 February 2023}}</ref><ref>{{Cite journal \|last=Minsky \|first=Marvin \|date=1961-01 \|title=Steps toward Artificial Intelligence \|url=http://ieeexplore.ieee.org/document/4066245/ \|journal=Proceedings of the IRE \|volume=49 \|issue=1 \|pages=8–30 \|doi=10.1109/JRPROC.1961.287775 \|issn=0096-8390}}</ref>

			*Today, AI is practically being used in not only clinical laboratories but also clinical research labs, as well as life science and research and development (R&D) labs. Practical uses of AI can be found in:

			:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web \|url=https://siim.org/page/22w_clinical_adoption_of_ai \|title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties \|author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. \|publisher=Society for Imaging Informatics in Medicine \|date=11 October 2022}}</ref>
			:ophthalmology clinics<ref>{{Cite journal \|last=He \|first=Mingguang \|last2=Li \|first2=Zhixi \|last3=Liu \|first3=Chi \|last4=Shi \|first4=Danli \|last5=Tan \|first5=Zachary \|date=2020-07 \|title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge \|url=https://journals.lww.com/10.1097/APO.0000000000000301 \|journal=Asia-Pacific Journal of Ophthalmology \|language=en \|volume=9 \|issue=4 \|pages=299–307 \|doi=10.1097/APO.0000000000000301 \|issn=2162-0989}}</ref>
			:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>
			:material testing labs<ref>{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref>{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>
			:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref>{{Cite journal \|last=Burger \|first=Benjamin \|last2=Maffettone \|first2=Phillip M. \|last3=Gusev \|first3=Vladimir V. \|last4=Aitchison \|first4=Catherine M. \|last5=Bai \|first5=Yang \|last6=Wang \|first6=Xiaoyan \|last7=Li \|first7=Xiaobo \|last8=Alston \|first8=Ben M. \|last9=Li \|first9=Buyi \|last10=Clowes \|first10=Rob \|last11=Rankin \|first11=Nicola \|date=2020-07-09 \|title=A mobile robotic chemist \|url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org \|journal=Nature \|language=en \|volume=583 \|issue=7815 \|pages=237–241 \|doi=10.1038/s41586-020-2442-2 \|issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal \|last=Lemonick, S. \|date=06 April 2020 \|title=Exploring chemical space: Can AI take us where no human has gone before? \|url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|journal=Chemical & Engineering News \|volume=98 \|issue=13 \|archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|archivedate=29 July 2020 \|accessdate=16 February 2023}}</ref>


	==References==		==References==
	{{Reflist\|colwidth=30em}}		{{Reflist\|colwidth=30em}}

← Older revision		Revision as of 23:54, 16 February 2023
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	:reproduction clinics<ref>{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref><ref name="ESHREArti22">{{cite web \|url=https://www.focusonreproduction.eu/article/ESHRE-News-22AI \|title=Annual Meeting 2022: Artificial intelligence in embryology and ART \|author=European Society of Human Reproduction and Embryology \|work=Focus on Reproduction \|date=06 July 2022 \|accessdate=16 February 2023}}</ref><ref name="HinckleyApply21">{{cite web \|url=https://rscbayarea.com/blog/applying-ai-for-better-ivf-success \|title=Applying AI (Artificial Intelligence) in the Lab for Better IVF Success \|author=Hinckley, M. \|work=Reproductive Science Center Blog \|publisher=Reproductive Science Center of the Bay Area \|date=17 March 2021 \|accessdate=16 February 2023}}</ref>		:reproduction clinics<ref>{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref><ref name="ESHREArti22">{{cite web \|url=https://www.focusonreproduction.eu/article/ESHRE-News-22AI \|title=Annual Meeting 2022: Artificial intelligence in embryology and ART \|author=European Society of Human Reproduction and Embryology \|work=Focus on Reproduction \|date=06 July 2022 \|accessdate=16 February 2023}}</ref><ref name="HinckleyApply21">{{cite web \|url=https://rscbayarea.com/blog/applying-ai-for-better-ivf-success \|title=Applying AI (Artificial Intelligence) in the Lab for Better IVF Success \|author=Hinckley, M. \|work=Reproductive Science Center Blog \|publisher=Reproductive Science Center of the Bay Area \|date=17 March 2021 \|accessdate=16 February 2023}}</ref>
	:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>		:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>
	:material testing labs<ref>{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref>{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>		:material testing labs<ref name=":2">{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref name=":3">{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>
	:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref>{{Cite journal \|last=Burger \|first=Benjamin \|last2=Maffettone \|first2=Phillip M. \|last3=Gusev \|first3=Vladimir V. \|last4=Aitchison \|first4=Catherine M. \|last5=Bai \|first5=Yang \|last6=Wang \|first6=Xiaoyan \|last7=Li \|first7=Xiaobo \|last8=Alston \|first8=Ben M. \|last9=Li \|first9=Buyi \|last10=Clowes \|first10=Rob \|last11=Rankin \|first11=Nicola \|date=2020-07-09 \|title=A mobile robotic chemist \|url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org \|journal=Nature \|language=en \|volume=583 \|issue=7815 \|pages=237–241 \|doi=10.1038/s41586-020-2442-2 \|issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal \|last=Lemonick, S. \|date=06 April 2020 \|title=Exploring chemical space: Can AI take us where no human has gone before? \|url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|journal=Chemical & Engineering News \|volume=98 \|issue=13 \|archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|archivedate=29 July 2020 \|accessdate=16 February 2023}}</ref>		:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref>{{Cite journal \|last=Burger \|first=Benjamin \|last2=Maffettone \|first2=Phillip M. \|last3=Gusev \|first3=Vladimir V. \|last4=Aitchison \|first4=Catherine M. \|last5=Bai \|first5=Yang \|last6=Wang \|first6=Xiaoyan \|last7=Li \|first7=Xiaobo \|last8=Alston \|first8=Ben M. \|last9=Li \|first9=Buyi \|last10=Clowes \|first10=Rob \|last11=Rankin \|first11=Nicola \|date=2020-07-09 \|title=A mobile robotic chemist \|url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org \|journal=Nature \|language=en \|volume=583 \|issue=7815 \|pages=237–241 \|doi=10.1038/s41586-020-2442-2 \|issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal \|last=Lemonick, S. \|date=06 April 2020 \|title=Exploring chemical space: Can AI take us where no human has gone before? \|url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|journal=Chemical & Engineering News \|volume=98 \|issue=13 \|archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|archivedate=29 July 2020 \|accessdate=16 February 2023}}</ref>
	:quantum physics labs<ref name="DoctrowArti19">{{cite web \|url=https://www.pnas.org/post/podcast/artificial-intelligence-laboratory \|title=Artificial intelligence in the laboratory \|author=Doctrow, B. \|work=PNAS Science Sessions \|date=16 December 2019 \|accessdate=16 February 2023}}</ref>		:quantum physics labs<ref name="DoctrowArti19">{{cite web \|url=https://www.pnas.org/post/podcast/artificial-intelligence-laboratory \|title=Artificial intelligence in the laboratory \|author=Doctrow, B. \|work=PNAS Science Sessions \|date=16 December 2019 \|accessdate=16 February 2023}}</ref>

			*What's going on in these labs?

			:Materials lab: The creation of "a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions ..."<ref name=":2" />
			:Materials lab: "Most of the applications of [machine learning (ML)] in chemical and materials sciences, as we have said, feature supervised learning algorithms. The goal there is to supplement or replace traditional modeling methods, at the quantum chemical or classical level, in order to predict the properties of molecules or materials directly from their structure or their chemical composition ... Our research group was applying the same idea on a narrower range of materials, trying to confirm that for a given chemical composition, geometrical descriptors of a material’s structure could lead to accurate predictions of its mechanical features."<ref name=":3" />

	==References==		==References==
	{{Reflist\|colwidth=30em}}		{{Reflist\|colwidth=30em}}

← Older revision		Revision as of 23:38, 16 February 2023
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	:clinical research labs<ref name=":0">{{Cite journal \|last=Damiani \|first=A. \|last2=Masciocchi \|first2=C. \|last3=Lenkowicz \|first3=J. \|last4=Capocchiano \|first4=N. D. \|last5=Boldrini \|first5=L. \|last6=Tagliaferri \|first6=L. \|last7=Cesario \|first7=A. \|last8=Sergi \|first8=P. \|last9=Marchetti \|first9=A. \|last10=Luraschi \|first10=A. \|last11=Patarnello \|first11=S. \|date=2021-12-07 \|title=Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator \|url=https://www.frontiersin.org/articles/10.3389/fcomp.2021.768266/full \|journal=Frontiers in Computer Science \|volume=3 \|pages=768266 \|doi=10.3389/fcomp.2021.768266 \|issn=2624-9898}}</ref>		:clinical research labs<ref name=":0">{{Cite journal \|last=Damiani \|first=A. \|last2=Masciocchi \|first2=C. \|last3=Lenkowicz \|first3=J. \|last4=Capocchiano \|first4=N. D. \|last5=Boldrini \|first5=L. \|last6=Tagliaferri \|first6=L. \|last7=Cesario \|first7=A. \|last8=Sergi \|first8=P. \|last9=Marchetti \|first9=A. \|last10=Luraschi \|first10=A. \|last11=Patarnello \|first11=S. \|date=2021-12-07 \|title=Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator \|url=https://www.frontiersin.org/articles/10.3389/fcomp.2021.768266/full \|journal=Frontiers in Computer Science \|volume=3 \|pages=768266 \|doi=10.3389/fcomp.2021.768266 \|issn=2624-9898}}</ref>
	:hospitals<ref name=":0" /><ref name=":1">{{Cite journal \|last=University of California, San Francisco \|last2=Adler-Milstein \|first2=Julia \|last3=Aggarwal \|first3=Nakul \|last4=University of Wisconsin-Madison \|last5=Ahmed \|first5=Mahnoor \|last6=National Academy of Medicine \|last7=Castner \|first7=Jessica \|last8=Castner Incorporated \|last9=Evans \|first9=Barbara J. \|last10=University of Florida \|last11=Gonzalez \|first11=Andrew A. \|date=2022-09-29 \|title=Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis \|url=https://nam.edu/meeting-the-moment-addressing-barriers-and-facilitating-clinical-adoption-of-artificial-intelligence-in-medical-diagnosis \|journal=NAM Perspectives \|volume=22 \|issue=9 \|doi=10.31478/202209c \|pmc=PMC9875857 \|pmid=36713769}}</ref>		:hospitals<ref name=":0" /><ref name=":1">{{Cite journal \|last=University of California, San Francisco \|last2=Adler-Milstein \|first2=Julia \|last3=Aggarwal \|first3=Nakul \|last4=University of Wisconsin-Madison \|last5=Ahmed \|first5=Mahnoor \|last6=National Academy of Medicine \|last7=Castner \|first7=Jessica \|last8=Castner Incorporated \|last9=Evans \|first9=Barbara J. \|last10=University of Florida \|last11=Gonzalez \|first11=Andrew A. \|date=2022-09-29 \|title=Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis \|url=https://nam.edu/meeting-the-moment-addressing-barriers-and-facilitating-clinical-adoption-of-artificial-intelligence-in-medical-diagnosis \|journal=NAM Perspectives \|volume=22 \|issue=9 \|doi=10.31478/202209c \|pmc=PMC9875857 \|pmid=36713769}}</ref>
	:medical diagnostics labs<ref name=":1" /><ref>{{Cite web \|last=Government Accountability Office (GAO); National Academy of Medicine (NAM) \|date=September 2022 \|title=Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics \|url=https://www.gao.gov/assets/gao-22-104629.pdf \|format=PDF \|publisher=Government Accountability Office \|accessdate=16 February 2023}}</ref><ref>{{Cite journal \|last=Wen \|first=Xiaoxia \|last2=Leng \|first2=Ping \|last3=Wang \|first3=Jiasi \|last4=Yang \|first4=Guishu \|last5=Zu \|first5=Ruiling \|last6=Jia \|first6=Xiaojiong \|last7=Zhang \|first7=Kaijiong \|last8=Mengesha \|first8=Birga Anteneh \|last9=Huang \|first9=Jian \|last10=Wang \|first10=Dongsheng \|last11=Luo \|first11=Huaichao \|date=2022-09-24 \|title=Clinlabomics: leveraging clinical laboratory data by data mining strategies \|url=https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04926-1 \|journal=BMC Bioinformatics \|language=en \|volume=23 \|issue=1 \|pages=387 \|doi=10.1186/s12859-022-04926-1 \|issn=1471-2105 \|pmc=PMC9509545 \|pmid=36153474}}</ref><ref>{{Cite journal \|last=DeYoung \|first=B. \|last2=Morales \|first2=M. \|last3=Giglio \|first3=S. \|date=2022-08-04 \|title=Microbiology 2.0–A “behind the scenes” consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories \|url=https://www.frontiersin.org/articles/10.3389/fmicb.2022.976068/full \|journal=Frontiers in Microbiology \|volume=13 \|pages=976068 \|doi=10.3389/fmicb.2022.976068 \|issn=1664-302X \|pmc=PMC9386241 \|pmid=35992715}}</ref><ref>{{Cite web \|last=Schut, M. \|date=01 December 2022 \|title=Get better with bytes \|url=https://www.amsterdamumc.org/en/research/news/get-better-with-bytes.htm \|publisher=Amsterdam UMC \|accessdate=16 February 2023}}</ref>		:medical diagnostics labs<ref name=":1" /><ref>{{Cite web \|last=Government Accountability Office (GAO); National Academy of Medicine (NAM) \|date=September 2022 \|title=Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics \|url=https://www.gao.gov/assets/gao-22-104629.pdf \|format=PDF \|publisher=Government Accountability Office \|accessdate=16 February 2023}}</ref><ref>{{Cite journal \|last=Wen \|first=Xiaoxia \|last2=Leng \|first2=Ping \|last3=Wang \|first3=Jiasi \|last4=Yang \|first4=Guishu \|last5=Zu \|first5=Ruiling \|last6=Jia \|first6=Xiaojiong \|last7=Zhang \|first7=Kaijiong \|last8=Mengesha \|first8=Birga Anteneh \|last9=Huang \|first9=Jian \|last10=Wang \|first10=Dongsheng \|last11=Luo \|first11=Huaichao \|date=2022-09-24 \|title=Clinlabomics: leveraging clinical laboratory data by data mining strategies \|url=https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04926-1 \|journal=BMC Bioinformatics \|language=en \|volume=23 \|issue=1 \|pages=387 \|doi=10.1186/s12859-022-04926-1 \|issn=1471-2105 \|pmc=PMC9509545 \|pmid=36153474}}</ref><ref>{{Cite journal \|last=DeYoung \|first=B. \|last2=Morales \|first2=M. \|last3=Giglio \|first3=S. \|date=2022-08-04 \|title=Microbiology 2.0–A “behind the scenes” consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories \|url=https://www.frontiersin.org/articles/10.3389/fmicb.2022.976068/full \|journal=Frontiers in Microbiology \|volume=13 \|pages=976068 \|doi=10.3389/fmicb.2022.976068 \|issn=1664-302X \|pmc=PMC9386241 \|pmid=35992715}}</ref><ref>{{Cite web \|last=Schut, M. \|date=01 December 2022 \|title=Get better with bytes \|url=https://www.amsterdamumc.org/en/research/news/get-better-with-bytes.htm \|publisher=Amsterdam UMC \|accessdate=16 February 2023}}</ref><ref name="AlbanoCal19">{{cite web \|url=https://physicianslab.com/calculations-to-diagnosis-the-artificial-intelligence-shift-thats-already-happening/ \|title=Calculations to Diagnosis: The Artificial Intelligence Shift That’s Already Happening \|author=Albano, V.; Morris, C.; Kent, T. \|work=Physicians Lab \|date=06 December 2019 \|accessdate=16 February 2023}}</ref>
			:chromatography labs<ref name="AlbanoCal19" />
	:biology and life science labs<ref>{{Cite journal \|last=de Ridder \|first=Dick \|date=2019-01 \|title=Artificial intelligence in the lab: ask not what your computer can do for you \|url=https://onlinelibrary.wiley.com/doi/10.1111/1751-7915.13317 \|journal=Microbial Biotechnology \|language=en \|volume=12 \|issue=1 \|pages=38–40 \|doi=10.1111/1751-7915.13317 \|pmc=PMC6302702 \|pmid=30246499}}</ref>		:biology and life science labs<ref>{{Cite journal \|last=de Ridder \|first=Dick \|date=2019-01 \|title=Artificial intelligence in the lab: ask not what your computer can do for you \|url=https://onlinelibrary.wiley.com/doi/10.1111/1751-7915.13317 \|journal=Microbial Biotechnology \|language=en \|volume=12 \|issue=1 \|pages=38–40 \|doi=10.1111/1751-7915.13317 \|pmc=PMC6302702 \|pmid=30246499}}</ref>
	:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web \|url=https://siim.org/page/22w_clinical_adoption_of_ai \|title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties \|author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. \|publisher=Society for Imaging Informatics in Medicine \|date=11 October 2022}}</ref>		:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web \|url=https://siim.org/page/22w_clinical_adoption_of_ai \|title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties \|author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. \|publisher=Society for Imaging Informatics in Medicine \|date=11 October 2022}}</ref>
	:ophthalmology clinics<ref>{{Cite journal \|last=He \|first=Mingguang \|last2=Li \|first2=Zhixi \|last3=Liu \|first3=Chi \|last4=Shi \|first4=Danli \|last5=Tan \|first5=Zachary \|date=2020-07 \|title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge \|url=https://journals.lww.com/10.1097/APO.0000000000000301 \|journal=Asia-Pacific Journal of Ophthalmology \|language=en \|volume=9 \|issue=4 \|pages=299–307 \|doi=10.1097/APO.0000000000000301 \|issn=2162-0989}}</ref>		:ophthalmology clinics<ref>{{Cite journal \|last=He \|first=Mingguang \|last2=Li \|first2=Zhixi \|last3=Liu \|first3=Chi \|last4=Shi \|first4=Danli \|last5=Tan \|first5=Zachary \|date=2020-07 \|title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge \|url=https://journals.lww.com/10.1097/APO.0000000000000301 \|journal=Asia-Pacific Journal of Ophthalmology \|language=en \|volume=9 \|issue=4 \|pages=299–307 \|doi=10.1097/APO.0000000000000301 \|issn=2162-0989}}</ref>
	:reproduction clinics<ref>{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref>		:reproduction clinics<ref>{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref><ref name="ESHREArti22">{{cite web \|url=https://www.focusonreproduction.eu/article/ESHRE-News-22AI \|title=Annual Meeting 2022: Artificial intelligence in embryology and ART \|author=European Society of Human Reproduction and Embryology \|work=Focus on Reproduction \|date=06 July 2022 \|accessdate=16 February 2023}}</ref><ref name="HinckleyApply21">{{cite web \|url=https://rscbayarea.com/blog/applying-ai-for-better-ivf-success \|title=Applying AI (Artificial Intelligence) in the Lab for Better IVF Success \|author=Hinckley, M. \|work=Reproductive Science Center Blog \|publisher=Reproductive Science Center of the Bay Area \|date=17 March 2021 \|accessdate=16 February 2023}}</ref>
	:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>		:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>
	:material testing labs<ref>{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref>{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>		:material testing labs<ref>{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref>{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>

← Older revision		Revision as of 17:35, 16 February 2023
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	*Today, AI is practically being used in not only clinical laboratories but also clinical research labs, as well as life science and research and development (R&D) labs. Practical uses of AI can be found in:		*Today, AI is practically being used in not only clinical laboratories but also clinical research labs, as well as life science and research and development (R&D) labs. Practical uses of AI can be found in:

			:clinical research labs<ref name=":0">{{Cite journal \|last=Damiani \|first=A. \|last2=Masciocchi \|first2=C. \|last3=Lenkowicz \|first3=J. \|last4=Capocchiano \|first4=N. D. \|last5=Boldrini \|first5=L. \|last6=Tagliaferri \|first6=L. \|last7=Cesario \|first7=A. \|last8=Sergi \|first8=P. \|last9=Marchetti \|first9=A. \|last10=Luraschi \|first10=A. \|last11=Patarnello \|first11=S. \|date=2021-12-07 \|title=Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator \|url=https://www.frontiersin.org/articles/10.3389/fcomp.2021.768266/full \|journal=Frontiers in Computer Science \|volume=3 \|pages=768266 \|doi=10.3389/fcomp.2021.768266 \|issn=2624-9898}}</ref>
			:hospitals<ref name=":0" /><ref name=":1">{{Cite journal \|last=University of California, San Francisco \|last2=Adler-Milstein \|first2=Julia \|last3=Aggarwal \|first3=Nakul \|last4=University of Wisconsin-Madison \|last5=Ahmed \|first5=Mahnoor \|last6=National Academy of Medicine \|last7=Castner \|first7=Jessica \|last8=Castner Incorporated \|last9=Evans \|first9=Barbara J. \|last10=University of Florida \|last11=Gonzalez \|first11=Andrew A. \|date=2022-09-29 \|title=Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis \|url=https://nam.edu/meeting-the-moment-addressing-barriers-and-facilitating-clinical-adoption-of-artificial-intelligence-in-medical-diagnosis \|journal=NAM Perspectives \|volume=22 \|issue=9 \|doi=10.31478/202209c \|pmc=PMC9875857 \|pmid=36713769}}</ref>
			:medical diagnostics labs<ref name=":1" /><ref>{{Cite web \|last=Government Accountability Office (GAO); National Academy of Medicine (NAM) \|date=September 2022 \|title=Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics \|url=https://www.gao.gov/assets/gao-22-104629.pdf \|format=PDF \|publisher=Government Accountability Office \|accessdate=16 February 2023}}</ref><ref>{{Cite journal \|last=Wen \|first=Xiaoxia \|last2=Leng \|first2=Ping \|last3=Wang \|first3=Jiasi \|last4=Yang \|first4=Guishu \|last5=Zu \|first5=Ruiling \|last6=Jia \|first6=Xiaojiong \|last7=Zhang \|first7=Kaijiong \|last8=Mengesha \|first8=Birga Anteneh \|last9=Huang \|first9=Jian \|last10=Wang \|first10=Dongsheng \|last11=Luo \|first11=Huaichao \|date=2022-09-24 \|title=Clinlabomics: leveraging clinical laboratory data by data mining strategies \|url=https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04926-1 \|journal=BMC Bioinformatics \|language=en \|volume=23 \|issue=1 \|pages=387 \|doi=10.1186/s12859-022-04926-1 \|issn=1471-2105 \|pmc=PMC9509545 \|pmid=36153474}}</ref><ref>{{Cite journal \|last=DeYoung \|first=B. \|last2=Morales \|first2=M. \|last3=Giglio \|first3=S. \|date=2022-08-04 \|title=Microbiology 2.0–A “behind the scenes” consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories \|url=https://www.frontiersin.org/articles/10.3389/fmicb.2022.976068/full \|journal=Frontiers in Microbiology \|volume=13 \|pages=976068 \|doi=10.3389/fmicb.2022.976068 \|issn=1664-302X \|pmc=PMC9386241 \|pmid=35992715}}</ref><ref>{{Cite web \|last=Schut, M. \|date=01 December 2022 \|title=Get better with bytes \|url=https://www.amsterdamumc.org/en/research/news/get-better-with-bytes.htm \|publisher=Amsterdam UMC \|accessdate=16 February 2023}}</ref>
			:biology and life science labs<ref>{{Cite journal \|last=de Ridder \|first=Dick \|date=2019-01 \|title=Artificial intelligence in the lab: ask not what your computer can do for you \|url=https://onlinelibrary.wiley.com/doi/10.1111/1751-7915.13317 \|journal=Microbial Biotechnology \|language=en \|volume=12 \|issue=1 \|pages=38–40 \|doi=10.1111/1751-7915.13317 \|pmc=PMC6302702 \|pmid=30246499}}</ref>
	:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web \|url=https://siim.org/page/22w_clinical_adoption_of_ai \|title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties \|author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. \|publisher=Society for Imaging Informatics in Medicine \|date=11 October 2022}}</ref>		:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web \|url=https://siim.org/page/22w_clinical_adoption_of_ai \|title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties \|author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. \|publisher=Society for Imaging Informatics in Medicine \|date=11 October 2022}}</ref>
	:ophthalmology clinics<ref>{{Cite journal \|last=He \|first=Mingguang \|last2=Li \|first2=Zhixi \|last3=Liu \|first3=Chi \|last4=Shi \|first4=Danli \|last5=Tan \|first5=Zachary \|date=2020-07 \|title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge \|url=https://journals.lww.com/10.1097/APO.0000000000000301 \|journal=Asia-Pacific Journal of Ophthalmology \|language=en \|volume=9 \|issue=4 \|pages=299–307 \|doi=10.1097/APO.0000000000000301 \|issn=2162-0989}}</ref>		:ophthalmology clinics<ref>{{Cite journal \|last=He \|first=Mingguang \|last2=Li \|first2=Zhixi \|last3=Liu \|first3=Chi \|last4=Shi \|first4=Danli \|last5=Tan \|first5=Zachary \|date=2020-07 \|title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge \|url=https://journals.lww.com/10.1097/APO.0000000000000301 \|journal=Asia-Pacific Journal of Ophthalmology \|language=en \|volume=9 \|issue=4 \|pages=299–307 \|doi=10.1097/APO.0000000000000301 \|issn=2162-0989}}</ref>
			:reproduction clinics<ref>{{Cite journal \|last=Trolice \|first=Mark P. \|last2=Curchoe \|first2=Carol \|last3=Quaas \|first3=Alexander M \|date=2021-07 \|title=Artificial intelligence—the future is now \|url=https://link.springer.com/10.1007/s10815-021-02272-4 \|journal=Journal of Assisted Reproduction and Genetics \|language=en \|volume=38 \|issue=7 \|pages=1607–1612 \|doi=10.1007/s10815-021-02272-4 \|issn=1058-0468 \|pmc=PMC8260235 \|pmid=34231110}}</ref>
	:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>		:digital pathology labs<ref name="YousifArt21">{{cite web \|url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ \|title=Artificial intelligence is the key driver for digital pathology adoption \|author=Yousif, M.; McClintock, D.S.; Yao, K. \|work=Clinical Laboratory Int \|publisher=PanGlobal Media \|date=2021 \|accessdate=16 February 2023}}</ref>
	:material testing labs<ref>{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref>{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>		:material testing labs<ref>{{Cite journal \|last=MacLeod \|first=B. P. \|last2=Parlane \|first2=F. G. L. \|last3=Morrissey \|first3=T. D. \|last4=Häse \|first4=F. \|last5=Roch \|first5=L. M. \|last6=Dettelbach \|first6=K. E. \|last7=Moreira \|first7=R. \|last8=Yunker \|first8=L. P. E. \|last9=Rooney \|first9=M. B. \|last10=Deeth \|first10=J. R. \|last11=Lai \|first11=V. \|date=2020-05-15 \|title=Self-driving laboratory for accelerated discovery of thin-film materials \|url=https://www.science.org/doi/10.1126/sciadv.aaz8867 \|journal=Science Advances \|language=en \|volume=6 \|issue=20 \|pages=eaaz8867 \|doi=10.1126/sciadv.aaz8867 \|issn=2375-2548 \|pmc=PMC7220369 \|pmid=32426501}}</ref><ref>{{Cite journal \|last=Chibani \|first=Siwar \|last2=Coudert \|first2=François-Xavier \|date=2020-08-01 \|title=Machine learning approaches for the prediction of materials properties \|url=http://aip.scitation.org/doi/10.1063/5.0018384 \|journal=APL Materials \|language=en \|volume=8 \|issue=8 \|pages=080701 \|doi=10.1063/5.0018384 \|issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal \|last=Mullin, R. \|date=28 March 2021 \|title=The lab of the future is now \|url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|journal=Chemical & Engineering News \|volume=99 \|issue=11 \|archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 \|archivedate=06 May 2022 \|accessdate=16 February 2023}}</ref>
	:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref>{{Cite journal \|last=Burger \|first=Benjamin \|last2=Maffettone \|first2=Phillip M. \|last3=Gusev \|first3=Vladimir V. \|last4=Aitchison \|first4=Catherine M. \|last5=Bai \|first5=Yang \|last6=Wang \|first6=Xiaoyan \|last7=Li \|first7=Xiaobo \|last8=Alston \|first8=Ben M. \|last9=Li \|first9=Buyi \|last10=Clowes \|first10=Rob \|last11=Rankin \|first11=Nicola \|date=2020-07-09 \|title=A mobile robotic chemist \|url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org \|journal=Nature \|language=en \|volume=583 \|issue=7815 \|pages=237–241 \|doi=10.1038/s41586-020-2442-2 \|issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal \|last=Lemonick, S. \|date=06 April 2020 \|title=Exploring chemical space: Can AI take us where no human has gone before? \|url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|journal=Chemical & Engineering News \|volume=98 \|issue=13 \|archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|archivedate=29 July 2020 \|accessdate=16 February 2023}}</ref>		:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref>{{Cite journal \|last=Burger \|first=Benjamin \|last2=Maffettone \|first2=Phillip M. \|last3=Gusev \|first3=Vladimir V. \|last4=Aitchison \|first4=Catherine M. \|last5=Bai \|first5=Yang \|last6=Wang \|first6=Xiaoyan \|last7=Li \|first7=Xiaobo \|last8=Alston \|first8=Ben M. \|last9=Li \|first9=Buyi \|last10=Clowes \|first10=Rob \|last11=Rankin \|first11=Nicola \|date=2020-07-09 \|title=A mobile robotic chemist \|url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org \|journal=Nature \|language=en \|volume=583 \|issue=7815 \|pages=237–241 \|doi=10.1038/s41586-020-2442-2 \|issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal \|last=Lemonick, S. \|date=06 April 2020 \|title=Exploring chemical space: Can AI take us where no human has gone before? \|url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|journal=Chemical & Engineering News \|volume=98 \|issue=13 \|archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 \|archivedate=29 July 2020 \|accessdate=16 February 2023}}</ref>
			:quantum physics labs<ref name="DoctrowArti19">{{cite web \|url=https://www.pnas.org/post/podcast/artificial-intelligence-laboratory \|title=Artificial intelligence in the laboratory \|author=Doctrow, B. \|work=PNAS Science Sessions \|date=16 December 2019 \|accessdate=16 February 2023}}</ref>


	==References==		==References==
	{{Reflist\|colwidth=30em}}		{{Reflist\|colwidth=30em}}