{"id":"W4412399246","doi":"10.1371/journal.pdig.0000941","title":"Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review","year":2025,"lang":"en","type":"review","venue":"PLOS Digital Health","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Jewish General Hospital; McGill University","funders":"Canadian Institutes of Health Research","keywords":"PsycINFO; Scopus; Health care; MEDLINE; Inclusion (mineral); Checklist; Knowledge management; Diversity (politics); Thematic analysis; Systematic review; Psychology; Computer science; Political science; Qualitative research; Sociology; Social science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.1131408,0.001954966,0.006308291,0.03626861,0.00350025,0.01349036,0.003564812,0.005849331,0.003308351],"category_scores_gemma":[0.2677992,0.002178255,0.006812934,0.0317993,0.005146181,0.01648561,0.009641804,0.004717406,0.000742734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01124054,"about_ca_system_score_gemma":0.05775547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008428314,"about_ca_topic_score_gemma":0.01734518,"domain_scores_codex":[0.8909296,0.05470991,0.03359286,0.003086649,0.01605947,0.001621484],"domain_scores_gemma":[0.7044141,0.2416277,0.0212026,0.005744481,0.02557264,0.001438465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001018776,0.00005119018,0.001171647,0.6578563,0.001737968,0.0002648697,0.00766901,0.0005437235,0.0003616305,0.01805769,0.008667328,0.3035167],"study_design_scores_gemma":[0.0000209038,0.00003804029,0.0006657657,0.9438439,0.001876753,0.0001529111,0.001964776,0.0001414324,0.000122685,0.004324322,0.0468164,0.00003212602],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0012515,0.9821863,0.003426078,0.00687366,0.0009190949,0.001904957,0.0002303018,0.00002445867,0.003183591],"genre_scores_gemma":[0.0130445,0.9716573,0.007900918,0.002053766,0.0003844962,0.004243212,0.000316452,0.00002378123,0.0003755252],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9964352,"threshold_uncertainty_score":0.5983529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4545176602772733,"score_gpt":0.5626835661996638,"score_spread":0.1081659059223904,"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."}}