Difference between revisions of "Template:Article of the week"

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'''[[Health informatics]]''' (also called '''health care informatics''', '''healthcare informatics''', '''medical informatics''', '''nursing informatics''',  '''clinical informatics''', or '''biomedical informatics''') is a discipline at the intersection of [[information science]], computer science, and health care. It deals with the resources, devices, and methods required to optimize the "collection, storage, retrieval, [and] communication ... of health-related data, [[information]], and knowledge." Health informatics is applied to the areas of nursing, clinical care, dentistry, pharmacy, public health, occupational therapy, and biomedical research. Health informatics resources include not only computers but also clinical guidelines, formal medical terminologies, and information and communication systems.
'''"[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]"'''


Worldwide use of technology in medicine began in the early 1950s with the rise of computers. Medical informatics research units began to appear during the 1970s in Poland and in the U.S., with medical informatics conferences springing up as early as 1974. Since then the development of high-quality health informatics research, education, and infrastructure has been the goal of the U.S. and the European Union. Hundreds of datasets, publications, guidelines, specifications, meetings, conferences, and organizations around the world continue to shape what health informatics is today. ('''[[Health informatics |Full article...]]''')<br />
Full [[laboratory automation]] is revolutionizing work habits in an increasing number of clinical [[microbiology]] facilities worldwide, generating huge streams of [[Imaging|digital images]] for interpretation. Contextually, [[deep learning]] (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic [[Bacteria|bacterial]] [[Cell culture|culture]] plates, including presumptive [[pathogen]] identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... ('''[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'': [[Content delivery network]], [[Federally qualified health center]], [[Home health agency]]
{{flowlist |
* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]
* [[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]
* [[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]
}}

Latest revision as of 15:02, 3 June 2024

Fig1 Signoroni NatComm23 14.png

"Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology"

Full laboratory automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... (Full article...)
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