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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Keck Bioimaging Lab.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png|240px]]</div>
'''[[Bioimage informatics]]''' is a multidisciplinary sub-field of [[bioinformatics]] and computational biology that involves the development and use of computational techniques to analyze bioimages, especially cellular and molecular images, on a large scale fashion, with the goal of mining useful knowledge out of complicated and heterogeneous images and related metadata.
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


The field of bioimage informatics is somewhat related to [[Imaging informatics|medical imaging informatics]], in so much as some of the advances in that field have found their way to the technology of analyzing bioimages. However, "it is very challenging to directly apply existing medical image analysis methods to ... bioimage informatics problems." Some of the challenges bioimages pose to researchers include the difficulty of analyzing at the cellular and molecular scales, the large size of the files, and the amount of time required to manually analyze the files. These challenges require automatic high-throughput analysis techniques, novel algorithms, and advanced systems to deal with the tasks of processing, storing, visualizing, and mining bioimages. ('''[[Bioimage informatics|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'':
''Recently featured'': [[Biobank]], [[Translational research]], [[Rural health clinic]]
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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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