{"id":"W3158906097","doi":"10.2196/28921","title":"Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Corporate governance; Veterans Affairs; Trustworthiness; Applications of artificial intelligence; Computer science; Artificial intelligence; Scale (ratio); Health care; Data science; Engineering ethics; Knowledge management; Risk analysis (engineering); Medicine; Computer security; Political science; Business; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.1284081,0.0003994638,0.0009311775,0.004057489,0.003727048,0.00608997,0.001934424,0.003199568,0.01160641],"category_scores_gemma":[0.4636168,0.0004709178,0.001198639,0.005004716,0.0105056,0.004774762,0.005252965,0.003577809,0.0006072043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005552734,"about_ca_system_score_gemma":0.0127263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023933,"about_ca_topic_score_gemma":0.01005583,"domain_scores_codex":[0.8203216,0.1523918,0.008990085,0.003600828,0.01210027,0.002595339],"domain_scores_gemma":[0.2637068,0.6220181,0.07195456,0.01556524,0.02248511,0.004270145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002902752,0.0007886011,0.4498715,0.01422063,0.00313331,0.001116494,0.05244939,0.0006621769,0.0001393549,0.07518649,0.02093333,0.3785961],"study_design_scores_gemma":[0.001449557,0.002891199,0.4594981,0.1266199,0.004997715,0.004817459,0.1256109,0.001756129,0.001132713,0.1066331,0.1643479,0.0002454338],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.434862,0.2585,0.003846763,0.1921709,0.001333047,0.0007844392,0.001050429,0.00002098622,0.1074314],"genre_scores_gemma":[0.9339683,0.04986748,0.001100667,0.01334802,0.0006108901,0.0002407093,0.0002232405,0.00001350216,0.0006271433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1284081,"threshold_uncertainty_score":0.6790949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5211005707907579,"score_gpt":0.5946362071618861,"score_spread":0.07353563637112825,"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."}}