{"id":"W4402498672","doi":"10.1080/01442872.2024.2400922","title":"Toward responsible artificial intelligence in health: regulatory structures and power dynamics of the big tech industry in the United States","year":2024,"lang":"en","type":"article","venue":"Policy Studies","topic":"Healthcare cost, quality, practices","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"High tech; Dynamics (music); Power (physics); Big data; Business; Political science; Law; Computer science; Sociology; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02741721,0.0002634849,0.0003593051,0.001074377,0.006953956,0.01385487,0.001837463,0.01928935,0.001620109],"category_scores_gemma":[0.04461206,0.0003939898,0.000748907,0.001114769,0.02898763,0.008304453,0.004426158,0.01926209,0.0001961297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01180348,"about_ca_system_score_gemma":0.02380285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02377244,"about_ca_topic_score_gemma":0.03280085,"domain_scores_codex":[0.9776409,0.01329313,0.001043506,0.001926299,0.004278955,0.001817176],"domain_scores_gemma":[0.9424455,0.04568013,0.002985172,0.001478853,0.005447455,0.001962944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002107447,0.00001590095,0.0005285339,0.00004900686,0.000007440273,0.0001049871,0.004905091,0.0003539788,0.0001996752,0.958055,0.02828538,0.00747404],"study_design_scores_gemma":[0.00003955292,0.00005079664,0.002627893,0.0008643136,0.00002217558,0.000121413,0.006548733,0.001551079,0.0007214053,0.6137024,0.3736649,0.00008533993],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.007576969,0.004911508,0.003173961,0.9620363,0.001637533,0.00001635407,0.00001807733,0.00001530512,0.02061414],"genre_scores_gemma":[0.4745667,0.01098602,0.004433741,0.4931569,0.008140883,0.0001394879,0.00003190415,0.00005681979,0.008487555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02741721,"threshold_uncertainty_score":0.1449978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6532877393100885,"score_gpt":0.5888134222723451,"score_spread":0.06447431703774342,"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."}}