Algorithm-Based Clinical Decision Support: Evolving Regulatory Landscape and Best Practices for Local Oversight.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Annual Review of Biomedical Data Science Pub Date : 2025-04-21 DOI:10.1146/annurev-biodatasci-103123-094601

Anthony L Lin, Amanda B Parrish, Michael Cary, Christina Silcox, Suresh Balu, J Eric Jelovsek, Cara O'Brien, Michael Pencina, Eric Poon, Nicoleta J Economou-Zavlanos

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引用次数: 0

Abstract

The potential of algorithm-based clinical decision support (CDS) in healthcare continues to increase with the growing field of artificial intelligence (AI)-enabled CDS. The use of these technologies to support clinicians, patients, and health systems is still quite new, and to date, implementors and regulators are still identifying the best processes and practices to ensure the effective, safe, and equitable use of these technology solutions. To assist individuals and organizations interested in implementation of algorithm-based CDS and AI-enabled CDS in healthcare, this article reviews the important regulatory decisions that form the landscape within which algorithm-based CDS has emerged, modern governance frameworks used to oversee these CDS systems, nuances in evaluation and monitoring throughout the CDS life cycle, best practices for real-world implementation, safety and equity considerations, and avenues for future collaboration and innovation.

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基于算法的临床决策支持：不断发展的监管环境和地方监督的最佳实践。

随着人工智能（AI）支持的临床决策支持（CDS）领域的不断发展，基于算法的临床决策支持（CDS）在医疗保健领域的潜力不断增加。使用这些技术来支持临床医生、患者和卫生系统仍然是相当新的，迄今为止，实施者和监管机构仍在确定最佳流程和做法，以确保有效、安全和公平地使用这些技术解决方案。为了帮助对在医疗保健领域实施基于算法的CDS和支持ai的CDS感兴趣的个人和组织，本文回顾了形成基于算法的CDS出现的环境的重要监管决策、用于监督这些CDS系统的现代治理框架、整个CDS生命周期中评估和监控的细微差别、现实世界实施的最佳实践、安全和公平考虑。以及未来合作和创新的途径。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annual Review of Biomedical Data Science

CiteScore

11.10

自引率

1.70%

发文量

期刊介绍： The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.