Bibliographic Details
| Title: |
A scoping review of reporting gaps in FDA-approved AI medical devices |
| Authors: |
Vijaytha Muralidharan, Boluwatife Adeleye Adewale, Caroline J. Huang, Mfon Thelma Nta, Peter Oluwaduyilemi Ademiju, Pirunthan Pathmarajah, Man Kien Hang, Oluwafolajimi Adesanya, Ridwanullah Olamide Abdullateef, Abdulhammed Opeyemi Babatunde, Abdulquddus Ajibade, Sonia Onyeka, Zhou Ran Cai, Roxana Daneshjou, Tobi Olatunji |
| Source: |
npj Digital Medicine, Vol 7, Iss 1, Pp 1-9 (2024) |
| Publisher Information: |
Nature Portfolio, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Computer applications to medicine. Medical informatics |
| Subject Terms: |
Computer applications to medicine. Medical informatics, R858-859.7 |
| More Details: |
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% 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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2398-6352 |
| Relation: |
https://doaj.org/toc/2398-6352 |
| DOI: |
10.1038/s41746-024-01270-x |
| Access URL: |
https://doaj.org/article/243ac68a4348444db49d1cf5e67b63c8 |
| Accession Number: |
edsdoj.243ac68a4348444db49d1cf5e67b63c8 |
| Database: |
Directory of Open Access Journals |
