[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120925-en":3,"doc-seo-120925-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120925,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Should the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?","The article examines the ethics and regulation of adaptive machine learning (ML) systems in medicine, focusing on a question preceding the commonly discussed “update problem.” It argues that post-deployment continuous learning should be classified and regulated as medical research. Because such systems may change performance and parameters as they learn from new clinical data, patients receiving treatments involving these systems should be treated as research subjects. The paper outlines implications and possible regulatory responses.","The American Journal of Bioethics  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/uajb20)[www.tandfonline.com/journals/uajb20](homepage: www.tandfonline.com/journals/uajb20)  \nShould the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?  \nRobert Sparrow, Joshua Hatherley, Justin Oakley & Chris Bain  \nTo cite this article: Robert Sparrow, Joshua Hatherley, Justin Oakley & Chris Bain (25 Apr 2024): Should the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?, The American Journal of Bioethics, DOI: 10. 1080/15265161 .2024.2337429  \nTo link to this article: [https://doi.org/10.1080/15265161.2024.2337429](https://doi.org/10.1080/15265161.2024.2337429)  \n© 2024 The Author(s) . Published with license by Taylor & Francis Group, LLC.  \n\n|  Published online: 25 Apr 2024. |\n| --- |\n|  Submit your article to this journal  |\n|  View related articles  |\n|  View Crossmark data |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=uajb20](https://www.tandfonline.com/action/journalInformation?journalCode=uajb20)  \nThe AmericAn JournAl of BioeThics  \n[https://doi.org/10.1080/15265161.2024.2337429](https://doi.org/10.1080/15265161.2024.2337429)  \nTargeT arTicle    \nShould the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?  \nrobert Sparrow , Joshua Hatherley , Justin Oakley  and chris Bain   \nmonash university  \nABSTRACT  \na novel advantage of the use of machine learning (Ml) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory [approval. in](approval. in) this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical Ml systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects.  \nKEYWORDS  \nadaptive learning; continuous learning; ethics; machine  \nlearning; medicine; research  \nINTRODUCTION  \nMachine learning has the potential to generate exciting advances in medicine. It also raises many ethical issues (Char, Shah, and Magnus 2018; Grote and Berens 2020; Sparrow and Hatherley 2019; Svensson and Jotterand 2022; Vayena, Blasimme, and Cohen 2018) . In this paper we identify an ethical—and regulatory—question regarding the use of adaptive machine learning in medicine that has not, to our knowledge, previously been discussed. An advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from data gathered in clinical practice. To date, considerations of the ethical questions raised by the use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory approval. We draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous  \nlearning in medical ML systems should be categorized as research and that individuals whose treatment invo","cbCaitCUhYHX3HQh","https://ap.wps.com/l/cbCaitCUhYHX3HQh","pdf",1622251,1,13,"English","en",105,"# Introduction\n# Adaptive Machine Learning in Medicine\n## Ethical and regulatory classification of continuous learning","[{\"question\":\"What ethical issue does the paper prioritize beyond the “update problem”?\",\"answer\":\"It asks whether continuous learning that occurs after deploying adaptive ML systems should be classified and regulated as medical research.\"},{\"question\":\"Why does the authors’ view link adaptive ML use to research classification?\",\"answer\":\"Because these systems can keep learning from new clinical data after initial approval, meaning their operation changes in ways regulators must account for.\"},{\"question\":\"What treatment implication does the paper draw for individuals using such systems?\",\"answer\":\"Individuals whose care involves continuous-learning medical ML systems should be treated as research subjects.\"}]","Should the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research? 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