Evaluating transparency in AI/ML model characteristics for FDA-reviewed medical devices
Clear model and dataset documentation is essential for interpreting an AI device’s reported performance.
What the paper explores
This study assessed transparency in publicly available FDA device summaries across model-development and performance-reporting categories. Using an AI Characteristics Transparency Reporting score, the authors found limited disclosure across many of the assessed characteristics, despite modest improvement after the period associated with good machine learning practice principles.
How to interpret the evidence
The score describes public reporting, not the complete regulatory submission or the inherent quality of every model. Missing public information should not be treated as proof that a study was not performed.
Questions for MedTech teams
Document model scope, data provenance, evaluation methods and limitations in a traceable form that connects technical work with clinical and regulatory communication.
Sources & further reading
Read original paperDOI: 10.1038/s41746-025-02052-9
Bibliographic record and abstract on PubMedThis is a short editorial reading note on external research. The original research is attributed to its authors and does not represent an HMC study, product validation or regulatory determination.
