{"id":"W2914514892","doi":"10.1001/amajethics.2019.167","title":"Can AI Help Reduce Disparities in General Medical and Mental Health Care?","year":2019,"lang":"en","type":"article","venue":"The AMA Journal of Ethic","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":393,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute","funders":"Division of Graduate Education; National Institute of Mental Health","keywords":"Proxy (statistics); Socioeconomic status; Race (biology); Mental health; Health care; Unit (ring theory); Machine learning; Psychiatry; Medicine; Artificial intelligence; Actuarial science; Psychology; Computer science; Business; Political science; Environmental health","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":[],"consensus_categories":[],"category_scores_codex":[0.001329866,0.0000639177,0.0002195932,0.00008054804,0.00007713584,0.00001458109,0.00009005771,0.0000893478,0.00007374957],"category_scores_gemma":[0.0002337658,0.00004022708,0.00003810746,0.00008747642,0.0001151595,0.00005470669,0.00002046311,0.0008552191,0.000007048792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001507775,"about_ca_system_score_gemma":0.001158728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002811398,"about_ca_topic_score_gemma":0.001019509,"domain_scores_codex":[0.9987581,0.0001648568,0.0004357223,0.00007450727,0.0003870058,0.0001798047],"domain_scores_gemma":[0.999294,0.0001447881,0.0001425248,0.0001097533,0.0001276428,0.0001812909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001102294,0.0003206867,0.6380328,0.001106068,0.0001141215,0.00009495297,0.1533769,0.00004743879,0.002256027,0.01148878,0.01575628,0.1763036],"study_design_scores_gemma":[0.002975862,0.01025731,0.6766911,0.01038338,0.0001586018,0.01148764,0.2048927,0.003625866,0.01766719,0.03753008,0.02341937,0.0009108977],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7029971,0.003234183,0.000003978005,0.2930525,0.0005409474,0.0001117051,9.178229e-7,0.000002096585,0.00005660421],"genre_scores_gemma":[0.9845268,0.001968391,0.0000601644,0.01273174,0.0005572172,0.000001358954,0.000002695213,0.00000702976,0.0001446679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2815296,"threshold_uncertainty_score":0.4250011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07924484628818028,"score_gpt":0.4449371100620874,"score_spread":0.3656922637739071,"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."}}