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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Chang HealthInfoRes2023 29-4.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png|240px]]</div>
'''"[[Journal:Benefits of information technology in healthcare: Artificial intelligence, internet of things, and personal health records|Benefits of information technology in healthcare: Artificial intelligence, internet of things, and personal health records]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


Systematic evaluations of the benefits of [[health information technology]] (HIT) play an essential role in enhancing healthcare [[Quality (business)|quality]] by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of [[artificial intelligence]] (AI), the [[internet of things]] (IoT), and [[personal health record]]s (PHR), based on scientific evidence. The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major [[health informatics]] journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains ... ('''[[Journal:Benefits of information technology in healthcare: Artificial intelligence, internet of things, and personal health records|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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