{"id":"W4414015012","doi":"10.1148/ryai.09052025.podcast","title":"BRATS Africa: Building Inclusive AI in Radiology","year":2025,"lang":"en","type":"dataset","venue":"Radiology Artificial Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Radiology; Medical physics; Medicine; Computer science; Geography; Data science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.001246525,0.0006596419,0.001547828,0.001482083,0.0003149838,0.00004422434,0.0007437001,0.002027823,0.0009054845],"category_scores_gemma":[0.003351435,0.00066492,0.0002762096,0.001404083,0.0007549888,0.0001296728,0.0002875398,0.002677237,0.0003628652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009166502,"about_ca_system_score_gemma":0.002047409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002123711,"about_ca_topic_score_gemma":0.001763963,"domain_scores_codex":[0.9944361,0.0006175438,0.002096927,0.001338069,0.000303329,0.001208005],"domain_scores_gemma":[0.996052,0.001589352,0.0004304092,0.001192737,0.0004311295,0.0003043216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005002462,0.0004010798,0.0001496293,0.0003342717,0.0001045183,0.0003680931,0.0005307585,0.0002110329,0.0003835554,0.003060416,0.9500788,0.04387755],"study_design_scores_gemma":[0.00003669211,0.0008738546,0.0000988536,0.0008164977,0.0002691635,0.0005909111,0.0009344075,0.001084065,0.00730037,0.0403462,0.9468268,0.0008222556],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01053829,0.01608397,0.01366948,0.04305058,0.02652474,0.006809824,0.8807352,0.0004539569,0.002133936],"genre_scores_gemma":[0.04533089,0.006279052,0.001084891,0.009181677,0.004341019,0.0006095859,0.9322681,0.00006128868,0.0008435047],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05153287,"threshold_uncertainty_score":0.9996237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1093871306609012,"score_gpt":0.4464637501799323,"score_spread":0.3370766195190311,"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."}}