TRAM-AI: A Framework for Transparency in Reporting of Medical Imaging Datasets for Artificial Intelligence Development.
Authors
Affiliations (39)
Affiliations (39)
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India 160012.
- Institute of Diagnostic and Interventional Radiology Klinikum rechts der Isar School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.
- Program for Ocular Inflammation and Infection Translational Research (PROTON), National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore.
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
- Department of Radiology, Singapore Eye Research Institute, Singapore.
- Department of Radiology, Duke NUS Medical School, Singapore.
- Department of Radiology, National University of Singapore, Singapore.
- Department of Radiology, Perelman School of Medicine at University of Pennsylvania, Philadelphia, Pa.
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa.
- Independent Computer Scientist.
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Department of Radiology, Stanford University, Stanford, Calif.
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Md.
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
- Division of Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Basel, Basel, Switzerland.
- Department of Pediatric Radiology, University Children's Hospital Basel, Basel, Switzerland.
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy.
- Department of Radiology, Dasa, São Paulo, Brazil.
- Diagnostics Institute-Imaging, Cleveland Clinic Foundation, Cleveland, Ohio.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Mass.
- Department of Radiology, All India Institute of Medical Sciences, New Delhi, India.
- Department of Biomedical Informatics, Harvard Medical School, Boston, Mass.
- Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, Calif.
- Department of Bioengineering, UCLA Samueli School of Engineering, Los Angeles, Calif.
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Conn.
- Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece.
- Department of Radiology, Montefiore Medical Center, Bronx, NY.
- Departments of Radiology, Medicine, and Biomedical Data Science, Stanford University, Stanford, Calif.
- Department of Diagnostic Radiology and Centre of Cancer Medicine, University of Hong Kong, Hong Kong, China.
- Department of Radiology, NYU Grossman School of Medicine, New York, NY.
- Department of Radiology, Indian Institute of Science, Bengaluru, India.
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Tex.
- Department of Radiology, University of Maryland Medical Center, Baltimore, Md.
- Departments of Epidemiology, Imaging Physics, and Musculoskeletal Radiology, MD Anderson Cancer Center, Houston, Tex.
- Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, Calif.
- Department of Radiology, University of California, San Francisco, San Francisco, Calif.
- Department of Radiology, University of Cambridge, Cambridge, UK.
- Department of Radiology, University of Pennsylvania, Philadelphia, Pa.
Abstract
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 (<i>n</i> = 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.