{"id":"W4404402929","doi":"10.2196/63356","title":"EDAI Framework for Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of AI to Improve Health and Oral Health Care: Qualitative Study","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Shriners Hospitals for Children - Canada; Queen's University; McGill University; Mila - Quebec Artificial Intelligence Institute; Jewish General Hospital; Université de Montréal; McGill University Health Centre","funders":"Canadian Institutes of Health Research; Réseau de Recherche en Santé Buccodentaire et Osseuse","keywords":"Knowledge management; Process management; System lifecycle; Health care; Multidisciplinary approach; Inclusion (mineral); Data collection; Integrated care; Guideline; Computer science; Engineering; Management science; Application lifecycle management; Psychology; Medicine; Political 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":[],"consensus_categories":[],"category_scores_codex":[0.1070494,0.0007250386,0.001197738,0.005892781,0.009062537,0.006141787,0.003124673,0.002365099,0.002960421],"category_scores_gemma":[0.06656019,0.0007477205,0.001246132,0.005775913,0.01120222,0.008507369,0.01341876,0.003591954,0.0002868842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01752065,"about_ca_system_score_gemma":0.03968875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01272007,"about_ca_topic_score_gemma":0.01903588,"domain_scores_codex":[0.9418486,0.04884521,0.002553303,0.001670327,0.003139167,0.001943505],"domain_scores_gemma":[0.921446,0.0631878,0.003166332,0.001688176,0.008561775,0.00194995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003218686,0.00008175587,0.003365261,0.003730395,0.00002745895,0.000648112,0.9083949,0.0002796426,0.0005958776,0.03403103,0.003759876,0.04505356],"study_design_scores_gemma":[0.00002320713,0.0000523328,0.001906298,0.005836209,0.00003431754,0.0003844937,0.9421352,0.0005692394,0.0004248113,0.01025346,0.03833744,0.00004297584],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5478107,0.02245087,0.2361829,0.07974704,0.001247248,0.0386692,0.00399084,0.0002328038,0.06966835],"genre_scores_gemma":[0.7787898,0.008566605,0.1693012,0.007684549,0.00006603724,0.02913352,0.0007276629,0.00007805473,0.005652681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1070494,"threshold_uncertainty_score":0.5661381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3861958041221578,"score_gpt":0.6718134406377622,"score_spread":0.2856176365156043,"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."}}