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精准医学人工智能/机器学习时代美国实验室自建检测(LDTs)的患者安全与医疗质量

Patient safety and healthcare quality of U.S. laboratory developed tests (LDTs) in the AI/ML era of precision medicine.

作者信息

Kurnat-Thoma Emma L

机构信息

Georgetown Institute for Women, Peace and Security, Walsh School of Foreign Service, Georgetown University, Washington, DC, United States.

Precision Policy Solutions, LLC, Bethesda, MD, United States.

出版信息

Front Mol Biosci. 2024 Aug 5;11:1407513. doi: 10.3389/fmolb.2024.1407513. eCollection 2024.

Abstract

This policy brief summarizes current U.S. regulatory considerations for ensuring patient safety and health care quality of genetic/genomic test information for precision medicine in the era of artificial intelligence/machine learning (AI/ML). The critical role of innovative and efficient laboratory developed tests (LDTs) in providing accurate diagnostic genetic/genomic information for U.S. patient- and family-centered healthcare decision-making is significant. However, many LDTs are not fully vetted for sufficient analytic and clinical validity via current FDA and CMS regulatory oversight pathways. The U.S. Centers for Disease Control and Prevention's Policy Analytical Framework Tool was used to identify the issue, perform a high-level policy analysis, and develop overview recommendations for a bipartisan healthcare policy reform strategy acceptable to diverse precision and systems medicine stakeholders.

摘要

本政策简报总结了美国当前在人工智能/机器学习(AI/ML)时代确保精准医学中基因/基因组检测信息的患者安全和医疗质量的监管考量。创新且高效的实验室自建检测方法(LDTs)在为以美国患者和家庭为中心的医疗决策提供准确的诊断性基因/基因组信息方面发挥着关键作用。然而,许多LDTs并未通过美国食品药品监督管理局(FDA)和医疗保险与医疗补助服务中心(CMS)当前的监管监督途径进行充分的分析和临床有效性审查。美国疾病控制与预防中心的政策分析框架工具被用于识别该问题、进行高层次政策分析,并为不同的精准医学和系统医学利益相关者都能接受的两党医疗政策改革战略制定总体建议。

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