Selective Deployment of AI in Healthcare and the Problem of Declining Human Expertise.
Machine‐learning algorithms are transforming healthcare diagnostics and prognostics. However, they sometimes underperform for groups underrepresented in their training data. Vandersluis and Savulescu have suggested selectively deploying these algorithms for populations well represented in the traini...
| Publicado en: | Bioethics Vol. 39; no. 7; pp. 688 - 693 |
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| Formato: | Artículo |
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Wiley-Blackwell
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=188364334&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 188364334 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02699702 6PJ jtl: Bioethics issn: 02699702 maglogo: Y pubinfo: dt: Sep2025 vid: 39 iid: 7 pid: 480 pub: Wiley-Blackwell artinfo: ui: 188364334 10.1111/bioe.13424 ppf: 688 ppct: 5 formats: tig: atl: Selective Deployment of AI in Healthcare and the Problem of Declining Human Expertise. aug: au: Feldblyum Le Blevennec, Marie Kerguelen affil: Department of Philosophy, The University of Alabama, Tuscaloosa Alabama, , USA su: Health services accessibility Medical quality control Artificial intelligence Bioethics Clinical competence Health care industry Machine learning Algorithms sug: subj: Health services accessibility Medical quality control Artificial intelligence Bioethics Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology) All Other Health and Personal Care Stores Clinical competence Health care industry Machine learning Algorithms keyword: algorithms artificial intelligence bias expertise underrepresented groups algorithms artificial intelligence bias expertise underrepresented groups ab: Machine‐learning algorithms are transforming healthcare diagnostics and prognostics. However, they sometimes underperform for groups underrepresented in their training data. Vandersluis and Savulescu have suggested selectively deploying these algorithms for populations well represented in the training data, while excluding underrepresented groups until improvements are made to the algorithms. In this paper, I explore one long‐term risk of such selective deployment for certain small underrepresented groups, such as those with rare diseases. The risk in question is the potential long‐term decline in the human expertise critical for such small groups, which, because they are excluded from effective care by the algorithm, would still rely on non‐algorithmic, human expertise even in the long run. I then discuss how to best preserve human expertise and maintain long‐term access to quality care for excluded groups and contend that such expertise preservation is essential for ethical deployment of algorithmic processes in healthcare. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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