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.
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.
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。
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