Artificial intelligence (AI) and Machine learning (ML) systems in medicine are poised to significantly improve health care, for example, by offering earlier diagnoses of diseases or recommending optimally individualized treatment plans. However, the emergence of AI/ML in medicine also creates challenges, which regulators must pay attention to. Which medical AI/ML-based products should be reviewed by regulators? What evidence should be required to permit marketing for AI/ML-based software as a medical device (SaMD)? How can we ensure the safety and effectiveness of AI/ML-based SaMD that may change over time as they are applied to new data? The U.S. Food and Drug Administration (FDA), for example, has recently proposed a discussion paper to address some of these issues. But it misses an important point: we argue that regulators like the FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition.

医学领域的人工智能 (AI) 和机器学习 (ML) 系统有望显着改善医疗保健，例如，通过提供疾病的早期诊断或推荐最佳的个性化治疗计划。然而，AI/ML在医学领域的出现也带来了挑战，监管机构必须予以关注。监管机构应审查哪些基于人工智能/机器学习的医疗产品？需要哪些证据才能允许基于 AI/ML 的软件作为医疗设备 (SaMD) 进行营销？基于 AI/ML 的 SaMD 在应用于新数据时可能会随着时间的推移而发生变化，我们如何确保其安全性和有效性？例如，美国食品和药物管理局 (FDA) 最近提出了一份讨论文件来解决其中一些问题。但它忽略了重要的一点：我们认为 FDA 等监管机构需要扩大其范围，从评估基于人工智能/机器学习的医疗产品到评估系统。这种从产品视角到系统视角的转变对于最大限度地提高医疗保健中人工智能/机器学习的安全性和有效性至关重要，但它也给 FDA 等习惯于监管产品而不是系统的机构带来了重大挑战。我们为监管机构提供几项建议，以实现这一具有挑战性但重要的转变。