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

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'''"[[Journal:Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing|Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


Point-of-care testing (POCT) is one of the fastest growing disciplines in [[clinical laboratory]] medicine. POCT [[Medical device|devices]] are widely used in both acute and chronic patient management in the [[hospital]] and [[Physician office laboratory|primary care physician office]] settings. As demands for POCT in various healthcare settings increase, managing POCT testing quality and [[regulatory compliance]] are continually challenging. Despite technological advances in applying automatic system checks and built-in [[quality control]] to prevent analytical and operator errors, poor planning for POCT [[Interface (computing)|connectivity]] and [[Informatics (academic field)|informatics]] can limit [[Data sharing|data accessibility]] and [[Information management|management]] efficiency which impedes the utilization of POCT to its full potential. This article will summarize how connectivity and data management systems can improve timely access to POCT results, effective management of POCT programs, and ensure regulatory compliance. ('''[[Journal:Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing|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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Latest 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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