{"id":"W4210670486","doi":"10.2196/31623","title":"Governing Data and Artificial Intelligence for Health Care: Developing an International Understanding","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Wellcome Trust","keywords":"Public relations; Health care; Corporate governance; Focus group; Thematic analysis; General partnership; Health policy; Data governance; Political science; Qualitative research; Sociology; Business; Social science; Data quality; Marketing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.06197441,0.001095708,0.001163918,0.009539339,0.006499692,0.02794407,0.003234598,0.01364951,0.003314782],"category_scores_gemma":[0.03384226,0.0008821419,0.001116149,0.01067125,0.06393458,0.04444706,0.01508457,0.01725123,0.0006111422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01513103,"about_ca_system_score_gemma":0.02219364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004871891,"about_ca_topic_score_gemma":0.001626608,"domain_scores_codex":[0.9678245,0.02199595,0.002770598,0.002557316,0.003055939,0.001795801],"domain_scores_gemma":[0.9426632,0.04380977,0.00326324,0.00364948,0.004164366,0.002449996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004101145,0.00001434975,0.0004226929,0.0002692856,0.000005184719,0.00007438991,0.02789297,0.0002656536,0.00007092732,0.9597254,0.002549218,0.008705766],"study_design_scores_gemma":[0.000008481945,0.00002726572,0.001185616,0.005062367,0.00001628795,0.0003579824,0.06708962,0.001090716,0.0002399531,0.6011278,0.3237605,0.00003342488],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01606272,0.08536994,0.1215549,0.5429338,0.003170263,0.0004330154,0.0002676951,0.0001240631,0.2300836],"genre_scores_gemma":[0.6776277,0.1366759,0.1058543,0.06174561,0.004756894,0.001835145,0.0005885267,0.0002170717,0.01069891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9720559,"threshold_uncertainty_score":0.3277559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7100716238831042,"score_gpt":0.6225710140930583,"score_spread":0.08750060979004592,"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."}}