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The imperative for regulatory oversight of large language models (or generative AI) in healthcare.

作者信息

Meskó Bertalan, Topol Eric J

机构信息

The Medical Futurist Institute, Budapest, Hungary.

Department of Behavioural Sciences, Semmelweis University, Budapest, Hungary.

出版信息

NPJ Digit Med. 2023 Jul 6;6(1):120. doi: 10.1038/s41746-023-00873-0.


DOI:10.1038/s41746-023-00873-0
PMID:37414860
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10326069/
Abstract

The rapid advancements in artificial intelligence (AI) have led to the development of sophisticated large language models (LLMs) such as GPT-4 and Bard. The potential implementation of LLMs in healthcare settings has already garnered considerable attention because of their diverse applications that include facilitating clinical documentation, obtaining insurance pre-authorization, summarizing research papers, or working as a chatbot to answer questions for patients about their specific data and concerns. While offering transformative potential, LLMs warrant a very cautious approach since these models are trained differently from AI-based medical technologies that are regulated already, especially within the critical context of caring for patients. The newest version, GPT-4, that was released in March, 2023, brings the potentials of this technology to support multiple medical tasks; and risks from mishandling results it provides to varying reliability to a new level. Besides being an advanced LLM, it will be able to read texts on images and analyze the context of those images. The regulation of GPT-4 and generative AI in medicine and healthcare without damaging their exciting and transformative potential is a timely and critical challenge to ensure safety, maintain ethical standards, and protect patient privacy. We argue that regulatory oversight should assure medical professionals and patients can use LLMs without causing harm or compromising their data or privacy. This paper summarizes our practical recommendations for what we can expect from regulators to bring this vision to reality.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa37/10326069/6e5cfd7205c2/41746_2023_873_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa37/10326069/6e5cfd7205c2/41746_2023_873_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa37/10326069/6e5cfd7205c2/41746_2023_873_Fig1_HTML.jpg

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本文引用的文献

[1]
ChatGPT in healthcare: A taxonomy and systematic review.

Comput Methods Programs Biomed. 2024-3

[2]
Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine.

N Engl J Med. 2023-3-30

[3]
Patient Design: The Importance of Including Patients in Designing Health Care.

J Med Internet Res. 2022-8-31

[4]
The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database.

NPJ Digit Med. 2020-9-11

[5]
Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies.

BMJ. 2020-3-25

[6]
High-performance medicine: the convergence of human and artificial intelligence.

Nat Med. 2019-1-7

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