{"id":"W4382046279","doi":"10.2196/46344","title":"Data Science as a Core Competency in Undergraduate Medical Education in the Age of Artificial Intelligence in Health Care","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Vector Institute; Dalhousie University; University of Toronto; McMaster University","funders":"","keywords":"Curriculum; Core competency; Health care; Medical education; Core curriculum; Psychology; Engineering ethics; Medicine; Artificial intelligence; Computer science; Engineering; Pedagogy; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004347212,0.0001473476,0.0003228376,0.0009823644,0.0001079097,0.00002546749,0.000928204,0.0001960532,0.0001986137],"category_scores_gemma":[0.00957493,0.0001196579,0.0000323738,0.00420488,0.0005963154,0.0002598627,0.0001255285,0.0007683688,0.000093535],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005525736,"about_ca_system_score_gemma":0.0618969,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01149251,"about_ca_topic_score_gemma":0.02104941,"domain_scores_codex":[0.9956358,0.0002540274,0.001317327,0.0005638717,0.001738773,0.0004901942],"domain_scores_gemma":[0.9979488,0.0004123578,0.0002051608,0.000815657,0.0002447614,0.0003732136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.00007781675,0.002121872,0.03822555,0.0005854717,0.000002178587,0.00002263952,0.03504836,0.000006411054,0.00003754996,0.02623328,0.00265453,0.8949844],"study_design_scores_gemma":[0.0002870315,0.001072569,0.4064888,0.009983546,0.00002200927,0.0002018711,0.4556392,0.02427572,0.0003088462,0.08860253,0.01250982,0.0006080843],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.871676,0.0006513895,0.00008553665,0.1236389,0.001538805,0.001393853,0.00000459477,0.00003345916,0.0009773949],"genre_scores_gemma":[0.9915474,0.0007992008,0.0002913313,0.005967126,0.000386204,0.0002284384,0.0007263182,0.00001335312,0.00004064826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8943763,"threshold_uncertainty_score":0.9987679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1907731427032334,"score_gpt":0.5334429365098393,"score_spread":0.3426697938066059,"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."}}