AI可自动识别正常胸片，缓解英国放射科人力短缺问题。
Autonomous Reporting of Normal Chest X-rays by Artificial Intelligence in the United Kingdom. Can We Take the Human Out of the Loop?
- 用AI自动判读正常胸片，减少人工报告负担。
- 需解决正常标准定义、跨人群泛化及灵敏度特异度平衡问题。
- 适合关注医疗AI落地与监管的临床与政策决策者。
胸片（CXRs）是应用最广泛的影像检查。在英国，许多中心因放射科医生人手不足导致报告延迟。能够区分正常与异常胸片的人工智能（AI）工具应运而生，或可成为解决方案。若能安全地由AI自动识别并报告正常胸片，将显著减轻放射科工作量。本文探讨了自主AI报告正常胸片的可行性与影响。关键问题包括正常标准的界定、跨人群的泛化能力，以及灵敏度与特异性之间的权衡。同时涉及法律与监管挑战，如符合IR(ME)R和GDPR要求，以及错误责任框架缺失的问题。还需考虑对放射科医生实践的影响、市场后监测机制的建立，以及患者视角的纳入。尽管优势明显，但推广必须谨慎。
原文摘要 · Abstract (English)
Chest X-rays (CXRs) are the most commonly performed imaging investigation. In the UK, many centres experience reporting delays due to radiologist workforce shortages. Artificial intelligence (AI) tools capable of distinguishing normal from abnormal CXRs have emerged as a potential solution. If normal CXRs could be safely identified and reported without human input, a substantial portion of radiology workload could be reduced. This article examines the feasibility and implications of autonomous AI reporting of normal CXRs. Key issues include defining normal, ensuring generalisability across populations, and managing the sensitivity-specificity trade-off. It also addresses legal and regulatory challenges, such as compliance with IR(ME)R and GDPR, and the lack accountability frameworks for errors. Further considerations include the impact on radiologists practice, the need for robust post-market surveillance, and incorporation of patient perspectives. While the benefits are clear, adoption must be cautious.
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