The abstract is read at the publisher; the summary is JClub's.
Retrospective study designs dominate translation-relevant evidence in medical AI, with 158 of 247 original studies (64.0%) relying on retrospective data in clinical and translational journal publications.
- Why it matters: Even when medical AI performs well in controlled evaluations, translation into routine clinical practice is hindered by evidence gaps and context mismatches that limit trust and adoption.
- What they did: A systematic review of 247 original studies (searched within 2024–2025 dates across 31 clinical and translational journal titles) coded translation-relevant indicators, including external validation and label quality.
- The result: The review found additional limitations—125 studies (50.6%) with demographic or geographic data limits, 71 (28.7%) lacking qualifying independent external validation, and 33 (13.4%) with noisy or context-dependent labels—informing a clinical stewardship framework for shared responsibility.
The findingWhy it mattersWhat they didThe result
AI summary of the abstract · may contain errors · not medical advice · read the paper.