An extension to the FDA approval process is needed to achieve AI equity

Hammond, Alessandro; Jain, Bhav; Celi, Leo Anthony; Stanford, Fatima Cody

doi:10.1038/s42256-023-00614-8

Cited by 7 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(15–17) A lack of racial and ethnic profiling in publicly available regulatory documents risks further exacerbating this important health issue. (18,19) The FDA has recognized the potential for bias in AI/ML-based medical devices and has initiated action plans (“Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan”) in January 2021 (20,21) to address these concerns. However, despite these efforts, our study highlights reporting inconsistencies that may continue to propagate racial health disparities.…”

Section: Discussionmentioning

confidence: 99%

“…data collection and consideration of model performance across socioeconomic groups are paramount and must be incorporated in the assessment of market approval for emerging technologies (33). With only19.4% of devices providing information on the age of intended device users, our study suggests that the evaluation and approval process of medical AI devices by the FDA lacks comprehensive data on age diversity. Recent literature across specialties demonstrates differential performances in algorithms trained on adult or pediatric data (34,35).…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

Muralidharan,

Adewale,

Huang

et al. 2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Muralidharan,

Adewale,

Huang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“… 10 There is a growing sense of urgency within the academic, clinical, and regulatory communities to understand, monitor, and improve the effect of AI technologies through a health equity lens. 11 , 12 , 13 , 14 , 15 , 16 , 17 …”

Section: Introductionmentioning

confidence: 99%