{"id":"W4388007949","doi":"10.1145/3617694.3623224","title":"Taking Off with AI: Lessons from Aviation for Healthcare","year":2023,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Volkswagen Foundation; Canadian Institute for Advanced Research","keywords":"Aviation; Health care; Openness to experience; Incentive; Automation; Computer science; Risk analysis (engineering); Patient safety; Field (mathematics); Knowledge management; Engineering; Business; Psychology; Political science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.01344465,0.0006342748,0.0006055664,0.0009571827,0.004303456,0.008106963,0.001856509,0.00535531,0.005523245],"category_scores_gemma":[0.02391062,0.0002466426,0.0006961431,0.0007631934,0.01479467,0.007938017,0.005282569,0.009648686,0.001692456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006138723,"about_ca_system_score_gemma":0.009734225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01325554,"about_ca_topic_score_gemma":0.01623007,"domain_scores_codex":[0.992354,0.005377535,0.0002335051,0.0003516187,0.0009475941,0.0007356884],"domain_scores_gemma":[0.975534,0.01537059,0.000672041,0.001415752,0.003221631,0.003785929],"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":[0.0002842953,0.0003474517,0.01117666,0.001123503,0.0001407083,0.001648373,0.03465502,0.004503453,0.0006429196,0.3604869,0.2302665,0.3547242],"study_design_scores_gemma":[0.00007484901,0.0002520301,0.004257492,0.002657412,0.00004400276,0.0008789859,0.02542517,0.002605154,0.0008230914,0.524563,0.438324,0.00009482922],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.00987839,0.02174534,0.00881296,0.918198,0.002342548,0.00002917747,0.00005066217,0.0001332527,0.03880961],"genre_scores_gemma":[0.7211684,0.05032026,0.02396451,0.1802886,0.005564154,0.000169997,0.0001946089,0.0002761272,0.01805334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01344465,"threshold_uncertainty_score":0.07110292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3519693332891061,"score_gpt":0.5170702261964301,"score_spread":0.165100892907324,"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."}}