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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Johannes Cordua Arzt in seinem Studierzimmer.jpg|160px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png|240px]]</div>
A '''[[Physician office laboratory]]''' ('''POL''') is a physician-, partnership-, or group-maintained [[laboratory]] that performs diagnostic tests or examines specimens in order to diagnose, prevent, and/or treat a disease or impairment in a patient as part of the physician practice. The POL shows up in primary care physician offices as well as the offices of specialists like urologists, hematologists, gynecologists, and endocrinologists. In many countries like the United States, the physician office laboratory is considered a [[clinical laboratory]] and is thus regulated by federal, state, and/or local laws affecting such laboratories.
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


The workflow of a POL is similar to other clinical labs; the difference in workflows mostly comes down to the time spent in transporting the specimen to an outside lab and waiting for the processing. The in-office lab saves time in those parts of the process. Potential benefits of a POL include quicker access to test results for the clinician, greater efficiency of the clinical workflow, cheaper testing, and greater patient comfort and happiness. Potential disadvantages include the physician office being the only point-of-access, patients not feeling comfortable about the physician's office being the central repository of information, and the cost of meeting compliance requirements for local, state, and federal regulations. ('''[[Physician office laboratory|Full article...]]''')<br />
The introduction of [[ChatGPT]] has fuelled a public debate on the appropriateness of using generative [[artificial intelligence]] (AI) ([[large language model]]s or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (''N'' = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... ('''[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Full article...]]''')<br />
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''Recently featured'': [[United States Department of Health and Human Services]], [[Bioimage informatics]], [[Biobank]]
{{flowlist |
* [[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]
* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
}}

Revision as of 15:26, 20 May 2024

Fig1 Niszczota EconBusRev23 9-2.png

"Judgements of research co-created by generative AI: Experimental evidence"

The introduction of ChatGPT has fuelled a public debate on the appropriateness of using generative artificial intelligence (AI) (large language models or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (N = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... (Full article...)
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