{"id":"W4411117613","doi":"10.1101/2025.06.06.25328913","title":"Lack of children in public medical imaging data points to growing age bias in biomedical AI","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"National Institute of Biomedical Imaging and Bioengineering; Advanced Research Projects Agency; National Institutes of Health; Alliance de recherche numérique du Canada; Hospital for Sick Children","keywords":"Representation (politics); Health care; Population; Equity (law); Medical imaging; Medicine; Public health; Data science; Artificial intelligence; Computer science; Political science; Pathology; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003609208,0.0002277847,0.0006990508,0.00127288,0.00002792083,0.00002862034,0.0009761266,0.0004021571,0.0002991344],"category_scores_gemma":[0.009385737,0.0002164321,0.00007278252,0.0009886741,0.0001624399,0.0001409943,0.001505225,0.001505789,0.00004136876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002152556,"about_ca_system_score_gemma":0.002222965,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008226209,"about_ca_topic_score_gemma":0.001914548,"domain_scores_codex":[0.9962484,0.0002615744,0.001353553,0.0008165553,0.0008371625,0.0004827959],"domain_scores_gemma":[0.9976102,0.0003626042,0.0001451442,0.001309033,0.0001347184,0.0004382777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003854184,0.0003813769,0.9137732,0.0004730838,0.00002569171,0.0001430877,0.001213133,0.000003691357,0.0000473349,0.0001538026,0.00286803,0.08087903],"study_design_scores_gemma":[0.0004565804,0.0001078003,0.9587758,0.01310199,0.00009585444,0.00006408264,0.001443934,0.01092753,0.001834235,0.005063793,0.007482512,0.0006458961],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8627331,0.0003468511,0.0006417171,0.133913,0.001199735,0.0006977105,0.00005043904,0.00003847763,0.000378921],"genre_scores_gemma":[0.9904516,0.0002160608,0.0006533152,0.007075628,0.0005042305,0.00004640115,0.0009649783,0.00002085256,0.00006695601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1277184,"threshold_uncertainty_score":0.9989586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3220359159637519,"score_gpt":0.4868946938124666,"score_spread":0.1648587778487147,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}