Health Disparities and Reporting Gaps in Artificial Intelligence (AI) Enabled Medical Devices: A Scoping Review of 692 U.S. Food and Drug Administration (FDA) 510k Approvals
Murali, V.; Adewale, B. A.; Huang, C. J.; Nta, M. T.; Ademiju, P. O.; Pathmarajah, P.; Hang, M. K.; Adesanya, O.; Abdullateef, R. O.; Babatunde, A. O.; Ajibade, A.; Onyeka, S.; Cai, Z. R.; Daneshjou, R.; Olatunji, T.
Show abstract
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 510k-approved AI/ML-enabled medical devices 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.
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