TRAM-AI: A Framework for Transparency in Reporting of Medical Imaging Datasets for Artificial Intelligence Development.

Purpose To develop and evaluate TRAM-AI, a consensus-based checklist for verifying whether medical imaging datasets described as publicly accessible are functionally usable for AI development. Materials and Methods A modified Delphi study was conducted. A steering committee generated candidate checklist items through a targeted literature review. An international expert panel (n = 33) rated items on criticality and feasibility across two Delphi rounds. Consensus was defined as ≥ 75% agreement on both dimensions. The checklist was pilot-validated in 10 publicly available datasets. An advisory group of four experts from Latin America and sub-Saharan Africa evaluated cross-regional applicability. Results Of 16 candidate items, 14 were retained in the final checklist; one item was removed because of feasibility concerns, and another was merged with an existing item because of conceptual redundancy. All 14 final items achieved consensus for both criticality and feasibility. The final TRAM-AI checklist comprises 14 items across four domains: Dataset Access and Persistence (3 items), Technical Interoperability (4 items), Metadata and Reproducibility (4 items), and Ethical, Legal, and Maintenance considerations (3 items). In pilot validation, all items were applicable to all 10 datasets, compliance was 90% for trusted repository, de-dentification, and subject-level mapping, 10% for archive validation, and 30% for partition keys. All four advisory group members endorsed the checklist. Conclusion TRAM-AI provides a practical framework for verifying the functional accessibility of medical imaging datasets, complementing existing methodological reporting standards. Further evaluation and broader validation across diverse geographic settings are warranted. ©RSNA, 2026.