review · npj Digital Medicine
An examination of 692 artificial intelligence and machine learning medical devices approved by the FDA between 1995 and 2023 reveals major reporting gaps in safety, transparency, and demographic representation. Only 3.6 percent of these approvals reported participant race or ethnicity, and over 99 percent omitted socioeconomic data entirely. Furthermore, 81.6 percent failed to document participant age. Comprehensive performance results were provided in only 46.1 percent of approvals, and fewer than two percent linked directly to peer-reviewed scientific publications detailing safety and efficacy. In addition, prospective post-market surveillance studies were present in just 9.0 percent of devices. Because these healthcare technologies are entering clinical environments without consistent demographic and evaluative reporting, there is an elevated risk that algorithmic bias and existing health disparities will be amplified.
Artificial intelligence tools in healthcare risk worsening health disparities if their safety and effectiveness are not transparently verified across diverse patient groups. When regulatory records lack fundamental data on patient age, ethnicity, and socioeconomic background, clinicians and health systems cannot confirm whether a device performs equitably and safely in real-world clinical care.
The findings inform medical device developers, technology transfer offices, and regulatory teams preparing artificial intelligence products for commercial market approval. For commercial digital health products already at or approaching market readiness, developers must address clear deficits in trial reporting by integrating thorough demographic tracking, transparent performance metrics, and prospective post-market surveillance to satisfy regulatory scrutiny and gain clinical adoption.
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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.
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DOI: 10.1038/s41746-024-01270-x
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