Toward a responsible future: recommendations for AI-enabled clinical decision support

作者：Steven E. Labkoff, Bilikis Oladimeji, Joseph Kannry, Anthony Solomonides, Russell Leftwich, Eileen Koski, Amanda L. Joseph, Mónica López-González, Lee A. Fleisher, Kimberly Nolen, Sayon Dutta, Deborah R Levy, Amy Price, Paul Barr, Jonathan D. Hron, Baihan Lin, Gyana Srivastava, Núria Pastor, Unai Sánchez Luque, Tien Thi Thuy Bui, Reva Singh, T. A. Williams, Mark G. Weiner, Tristan Naumann, Dean F. Sittig, Gretchen Purcell Jackson, Yuri Quintana · 发表于：Journal of the American Medical Informatics Association · 年份：2024 · DOI：10.1093/jamia/ocae209 · 被引用次数：180 · 研究领域：Artificial Intelligence in Healthcare and Education、Explainable Artificial Intelligence (XAI)、Electronic Health Records Systems

BACKGROUND: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging. OBJECTIVES: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients. MATERIALS AND METHODS: In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process. RESULTS: Our work culminated in several...