A scoping review of reporting gaps in FDA-approved AI medical devices
Stanford Medicine · Stanford University · +9 more institutions
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-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%…
Citation impact
- FWCI
- 14.05
- Percentile
- 100%
- References
- 55
Authors
15- VMVijaytha MuralidharanCorresponding
Stanford Medicine, Stanford University
- BABoluwatife Adeleye Adewale
University of Ibadan, Babcock University
- CJCaroline J. Huang
Stanford University
- MTMfon Thelma Nta
Afe Babalola University
- POPeter Oluwaduyilemi Ademiju
University of Lagos, Lagos State Health Service Commission
Topics & keywords
- Medicine
- Medical physics