Machine learning and artificial intelligence (AI/ML) models in healthcare may exacerbate health biases. Regulatory oversight is critical in evaluating the safety and effectiveness of AI/ML devices in clinical settings. We conducted a scoping review on the 692 FDA-approved AI/ML-enabled medical devices approved from 1995-2023 to examine transparency, safety reporting, and sociodemographic representation. Only 3.6% of approvals reported race/ethnicity, 99.1% provided no socioeconomic data. 81.6% did not report the age of study subjects. Only 46.1% provided comprehensive detailed results of performance studies; only 1.9% included a link to a scientific publication with safety and efficacy data. Only 9.0% contained a prospective study for post-market surveillance. Despite the growing number of market-approved medical devices, our data shows that FDA reporting data remains inconsistent. Demographic and socioeconomic characteristics are underreported, exacerbating the risk of algorithmic bias and health disparity.

医疗保健领域的机器学习和人工智能 (AI/ML) 模型可能会加剧健康偏差。监管监督对于评估临床环境中人工智能/机器学习设备的安全性和有效性至关重要。我们对 1995 年至 2023 年期间批准的 692 种 FDA 批准的支持 AI/ML 的医疗设备进行了范围审查，以检查透明度、安全报告和社会人口统计代表性。只有 3.6% 的批准报告了种族/民族，99.1% 的批准没有提供社会经济数据。 81.6% 的人没有报告研究对象的年龄。只有 46.1% 提供了全面详细的绩效研究结果；只有 1.9% 包含带有安全性和有效性数据的科学出版物的链接。只有 9.0% 包含上市后监测的前瞻性研究。尽管市场批准的医疗器械数量不断增加，但我们的数据显示 FDA 报告数据仍然不一致。人口统计和社会经济特征被低估，加剧了算法偏差和健康差异的风险。