{"id":"W4297243326","doi":"10.1038/s41591-022-01987-w","title":"Tackling bias in AI health datasets through the STANDING Together initiative","year":2022,"lang":"en","type":"letter","venue":"Nature Medicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":81,"is_retracted":false,"has_abstract":false,"ca_institutions":"Google (Canada); Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"Medical Research Council; National Institute for Health and Care Research","keywords":"Data science; MEDLINE; Medicine; Computer science; Political science","routes":{"ca_aff":true,"ca_fund":false,"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":["research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001835098,0.0003736557,0.00084553,0.0003513304,0.0003548906,0.00001750471,0.0003347753,0.0009552437,0.002490609],"category_scores_gemma":[0.001605032,0.0002322223,0.00008720646,0.0009642173,0.0002389792,0.0001141947,0.00008774654,0.01352211,0.00002686119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062192,"about_ca_system_score_gemma":0.001115663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003935349,"about_ca_topic_score_gemma":0.0004580518,"domain_scores_codex":[0.9961236,0.0004937372,0.0009667868,0.0006079509,0.00115138,0.0006565365],"domain_scores_gemma":[0.9972159,0.001279833,0.000466109,0.0007740399,0.0001666341,0.00009751698],"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":[0.000093375,0.00003492947,0.0008300158,0.0004200991,0.00004700643,0.0003058384,0.01385364,0.000004280942,0.000002377007,0.000122202,0.9797743,0.004511941],"study_design_scores_gemma":[0.0001590191,0.0006248365,0.0001397641,0.001438967,0.00007219423,0.00009290103,0.007817068,0.00003071602,0.00006018081,0.001954908,0.9874275,0.0001819823],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0007008048,0.01589167,0.00009879957,0.976285,0.003802471,0.001263898,0.0002278404,0.00005268623,0.001676819],"genre_scores_gemma":[0.02047664,0.001492756,0.00009589057,0.9567952,0.01232027,0.0001158474,0.008055433,0.00007282262,0.0005751768],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01977583,"threshold_uncertainty_score":0.9984213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.275425114282323,"score_gpt":0.4974508012524966,"score_spread":0.2220256869701736,"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."}}