Jasmine Chiat Ling Ong PharmD , Shelley Yin-Hsi Chang MD , Wasswa William PhD , Prof Atul J Butte PhD , Prof Nigam H Shah PhD , Lita Sui Tjien Chew MMedSc , Nan Liu PhD , Prof Finale Doshi-Velez PhD , Wei Lu PhD , Prof Julian Savulescu PhD , Daniel Shu Wei Ting PhD
{"title":"Ethical and regulatory challenges of large language models in medicine","authors":"Jasmine Chiat Ling Ong PharmD , Shelley Yin-Hsi Chang MD , Wasswa William PhD , Prof Atul J Butte PhD , Prof Nigam H Shah PhD , Lita Sui Tjien Chew MMedSc , Nan Liu PhD , Prof Finale Doshi-Velez PhD , Wei Lu PhD , Prof Julian Savulescu PhD , Daniel Shu Wei Ting PhD","doi":"10.1016/S2589-7500(24)00061-X","DOIUrl":null,"url":null,"abstract":"<div><p>With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from the perspectives of users, developers, and regulators, notably focusing on data privacy and rights of use, data provenance, intellectual property contamination, and broad applications and plasticity of LLMs. A comprehensive framework and mitigating strategies will be imperative for the responsible integration of LLMs into medical practice, ensuring alignment with ethical principles and safeguarding against potential societal risks.</p></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 6","pages":"Pages e428-e432"},"PeriodicalIF":23.8000,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S258975002400061X/pdfft?md5=39a73cb24f24224e1864903fab51b512&pid=1-s2.0-S258975002400061X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lancet Digital Health","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S258975002400061X","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from the perspectives of users, developers, and regulators, notably focusing on data privacy and rights of use, data provenance, intellectual property contamination, and broad applications and plasticity of LLMs. A comprehensive framework and mitigating strategies will be imperative for the responsible integration of LLMs into medical practice, ensuring alignment with ethical principles and safeguarding against potential societal risks.
期刊介绍:
The Lancet Digital Health publishes important, innovative, and practice-changing research on any topic connected with digital technology in clinical medicine, public health, and global health.
The journal’s open access content crosses subject boundaries, building bridges between health professionals and researchers.By bringing together the most important advances in this multidisciplinary field,The Lancet Digital Health is the most prominent publishing venue in digital health.
We publish a range of content types including Articles,Review, Comment, and Correspondence, contributing to promoting digital technologies in health practice worldwide.