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

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.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

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Data Availability

All data produced in the present work are contained in the manuscript

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