人工智能干预:改善临床结果依赖于人工智能开发和验证的因果方法。

IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of the American Medical Informatics Association Pub Date : 2025-01-07 DOI:10.1093/jamia/ocae301
Shalmali Joshi, Iñigo Urteaga, Wouter A C van Amsterdam, George Hripcsak, Pierre Elias, Benjamin Recht, Noémie Elhadad, James Fackler, Mark P Sendak, Jenna Wiens, Kaivalya Deshpande, Yoav Wald, Madalina Fiterau, Zachary Lipton, Daniel Malinsky, Madhur Nayan, Hongseok Namkoong, Soojin Park, Julia E Vogt, Rajesh Ranganath
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引用次数: 0

摘要

医疗人工智能(AI)的主要实践始于模型开发,通常使用最先进的人工智能,并使用AUROC和DICE评分等人工智能文献中的指标进行回顾性评估。然而,这些指标的良好表现可能不会转化为改善的临床结果。相反,我们主张通过使用人工智能对临床相关结果产生积极影响的最终目标向后工作,从而构建更好的开发管道,从而考虑模型开发和验证中的因果关系,从而构建更好的开发管道。医疗人工智能应该是“可操作的”,人工智能引起的行动变化应该改善结果。量化行为变化对结果的影响是因果推理。因此,医疗人工智能的开发、评估和验证应该考虑人工智能干预对临床相关结果的因果影响。从因果关系的角度来看,我们为医疗保健人工智能管道各个阶段的关键利益相关者提出了建议。我们的建议旨在增加人工智能对临床结果的积极影响。
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AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.

The primary practice of healthcare artificial intelligence (AI) starts with model development, often using state-of-the-art AI, retrospectively evaluated using metrics lifted from the AI literature like AUROC and DICE score. However, good performance on these metrics may not translate to improved clinical outcomes. Instead, we argue for a better development pipeline constructed by working backward from the end goal of positively impacting clinically relevant outcomes using AI, leading to considerations of causality in model development and validation, and subsequently a better development pipeline. Healthcare AI should be "actionable," and the change in actions induced by AI should improve outcomes. Quantifying the effect of changes in actions on outcomes is causal inference. The development, evaluation, and validation of healthcare AI should therefore account for the causal effect of intervening with the AI on clinically relevant outcomes. Using a causal lens, we make recommendations for key stakeholders at various stages of the healthcare AI pipeline. Our recommendations aim to increase the positive impact of AI on clinical outcomes.

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来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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