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利用生成式人工智能应对临床试验中长期挑战的政策框架。

A policy framework for leveraging generative AI to address enduring challenges in clinical trials.

作者信息

Liddicoat Johnathon Edward, Lenarczyk Gabriela, Aboy Mateo, Minssen Timo, Porsdam Mann Sebastian

机构信息

Dickson Poon School of Law, King's College London, London, UK.

Center for Advanced Studies in Bioscience Innovation Law (CeBIL), Faculty of Law, University of Copenhagen, Copenhagen, Denmark.

出版信息

NPJ Digit Med. 2025 Jan 15;8(1):33. doi: 10.1038/s41746-025-01440-5.

DOI:10.1038/s41746-025-01440-5
PMID:39815025
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11736117/
Abstract

Can artificial intelligence improve clinical trial design? Despite their importance in medicine, over 40% of trials involve flawed protocols. We introduce and propose the development of application-specific language models (ASLMs) for clinical trial design across three phases: ASLM development by regulatory agencies, customization by Health Technology Assessment bodies, and deployment to stakeholders. This strategy could enhance trial efficiency, inclusivity, and safety, leading to more representative, cost-effective clinical trials.

摘要

人工智能能否改善临床试验设计?尽管临床试验在医学中至关重要,但超过40%的试验存在有缺陷的方案。我们介绍并提议开发用于三个阶段临床试验设计的特定应用语言模型(ASLM):由监管机构进行ASLM开发,由卫生技术评估机构进行定制,以及向利益相关者进行部署。这一策略可以提高试验效率、包容性和安全性,从而带来更具代表性、更具成本效益的临床试验。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1775/11736117/9aafc81d98d4/41746_2025_1440_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1775/11736117/9aafc81d98d4/41746_2025_1440_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1775/11736117/9aafc81d98d4/41746_2025_1440_Fig1_HTML.jpg

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本文引用的文献

1
The path forward for large language models in medicine is open.医学领域大语言模型的未来发展道路是开放的。
NPJ Digit Med. 2024 Nov 27;7(1):339. doi: 10.1038/s41746-024-01344-w.
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Assessing the Risk of Bias in Randomized Clinical Trials With Large Language Models.使用大型语言模型评估随机临床试验的偏倚风险。
JAMA Netw Open. 2024 May 1;7(5):e2412687. doi: 10.1001/jamanetworkopen.2024.12687.
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Leveraging artificial intelligence to detect ethical concerns in medical research: a case study.利用人工智能检测医学研究中的伦理问题:一个案例研究。
J Med Ethics. 2025 Jan 23;51(2):126-134. doi: 10.1136/jme-2023-109767.
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Confronting legacies of underrepresentation in clinical trials: The case for greater diversity in research.直面临床试验代表性不足的遗留问题:推进研究工作多样性的必要性。
Neuron. 2022 Mar 2;110(5):746-748. doi: 10.1016/j.neuron.2021.12.008. Epub 2022 Jan 14.
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J Clin Epidemiol. 2021 Oct;138:219-226. doi: 10.1016/j.jclinepi.2021.05.018. Epub 2021 May 30.
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Nat Rev Drug Discov. 2021 Apr;20(4):245-246. doi: 10.1038/d41573-020-00205-x.
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Increasing value and reducing waste in research design, conduct, and analysis.提高研究设计、实施和分析的价值并减少浪费。
Lancet. 2014 Jan 11;383(9912):166-75. doi: 10.1016/S0140-6736(13)62227-8. Epub 2014 Jan 8.