A paradigm shift?—On the ethics of medical large language models.
After a wave of breakthroughs in image‐based medical diagnostics and risk prediction models, machine learning (ML) has turned into a normal science. However, prominent researchers are claiming that another paradigm shift in medical ML is imminent—due to most recent staggering successes of large lang...
| Published in: | Bioethics Vol. 38; no. 5; pp. 383 - 391 |
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| Main Authors: | , |
| Format: | Article |
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Wiley-Blackwell
Jun2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=177082973&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 177082973 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02699702 6PJ jtl: Bioethics issn: 02699702 maglogo: Y pubinfo: dt: Jun2024 vid: 38 iid: 5 pid: 480 pub: Wiley-Blackwell artinfo: ui: 177082973 10.1111/bioe.13283 ppf: 383 ppct: 8 formats: tig: atl: A paradigm shift?—On the ethics of medical large language models. aug: au: Grote, Thomas Berens, Philipp affil: Cluster of Excellence: "Machine Learning: New Perspectives for Science", University of Tübingen, Tübingen, Germany Hertie Institute for AI in Brain Health & Tübingen AI Center, Tübingen, Germany su: Debate Interprofessional relations Medical care Responsibility Privacy Bioethics Paradigms (Social sciences) Trust Honesty Medical ethics Disclosure Patient autonomy Natural language processing Attitudes of medical personnel Machine learning sug: subj: Debate Interprofessional relations Medical care Responsibility Privacy Bioethics Paradigms (Social sciences) Trust Honesty Medical ethics Disclosure Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology) Patient autonomy Natural language processing Attitudes of medical personnel Machine learning keyword: autonomy clinical evaluation machine learning transparency trust autonomy clinical evaluation machine learning transparency trust ab: After a wave of breakthroughs in image‐based medical diagnostics and risk prediction models, machine learning (ML) has turned into a normal science. However, prominent researchers are claiming that another paradigm shift in medical ML is imminent—due to most recent staggering successes of large language models—from single‐purpose applications toward generalist models, driven by natural language. This article investigates the implications of this paradigm shift for the ethical debate. Focusing on issues like trust, transparency, threats of patient autonomy, responsibility issues in the collaboration of clinicians and ML models, fairness, and privacy, it will be argued that the main problems will be continuous with the current debate. However, due to functioning of large language models, the complexity of all these problems increases. In addition, the article discusses some profound challenges for the clinical evaluation of large language models and threats to the reproducibility and replicability of studies about large language models in medicine due to corporate interests. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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