Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing.
Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing.
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医学中的人工智能：通过质量保证、质量控制和验收测试降低风险并最大化收益。
DOI：
10.1093/bjrai/ubae003
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
G
中科院分区：
文献类型：
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作者：
Mahmood,Usman;Shukla-Dave,Amita;Chan,Heang-Ping;Drukker,Karen;Samala,RaviK;Chen,Quan;Vergara,Daniel;Greenspan,Hayit;Petrick,Nicholas;Sahiner,Berkman;Huo,Zhimin;Summers,RonaldM;Cha,KennyH;Tourassi,Georgia;Deserno,ThomasM;G
人工智能 (AI) 工具在医学中的采用给现有的临床工作流程带来了挑战。本评论讨论了针对具体情况的质量保证 (QA) 的必要性，强调需要采用稳健的 QA 措施和质量控制 (QC) 程序，包括 (1) 临床使用前的验收测试 (AT)、(2) 持续的 QC 监测和 (3) 充分的用户培训。讨论还涵盖了 AT 和 QA 的基本组成部分，并通过现实世界的示例进行了说明。我们还强调了制造商或供应商、监管机构、医疗保健系统、医学物理学家和临床医生的共同责任，即制定适当的测试和监督，以确保通过人工智能实现医学的安全和公平转型。
The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples. We also highlight what we see as the shared responsibility of manufacturers or vendors, regulators, healthcare systems, medical physicists, and clinicians to enact appropriate testing and oversight to ensure a safe and equitable transformation of medicine through AI.