{"id":"W4283519332","doi":"10.1093/jamia/ocac103","title":"Impact of artificial intelligence on pathologists’ decisions: an experiment","year":2022,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Toronto Metropolitan University; Ted Rogers Centre for Heart Research","funders":"Social Sciences and Humanities Research Council","keywords":"Artificial intelligence; Generalization; Machine learning; Odds; Computer science; Process (computing); Medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01806181,0.0007546077,0.0007040855,0.000445792,0.0008791291,0.001745248,0.001287479,0.00203419,0.008183884],"category_scores_gemma":[0.07497274,0.0009065601,0.0007236755,0.0004302769,0.001359185,0.001914121,0.00180909,0.001841192,0.0009133923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007277417,"about_ca_system_score_gemma":0.001784165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004596709,"about_ca_topic_score_gemma":0.0004287754,"domain_scores_codex":[0.9837174,0.01069253,0.00151273,0.001529531,0.001724733,0.0008230243],"domain_scores_gemma":[0.7156689,0.2496616,0.01815838,0.009149337,0.003371282,0.003990493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"nonrandomized_trial","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.2087258,0.3348673,0.1913536,0.003297694,0.0009038497,0.0008126061,0.03265358,0.01079887,0.04625321,0.002992453,0.004408759,0.1629323],"study_design_scores_gemma":[0.06059305,0.5698786,0.2393569,0.0007219728,0.00101446,0.0006838809,0.008510217,0.04951471,0.04743474,0.007487048,0.0141889,0.0006155316],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996543,0.00002754219,0.0007462565,0.0001426559,0.0000336881,0.001230482,0.0001140998,0.00002346058,0.001138746],"genre_scores_gemma":[0.9818504,0.00007373016,0.00982312,0.0005307409,0.0000951082,0.005953116,0.0001844378,0.00001530198,0.001473944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01806181,"threshold_uncertainty_score":0.09552109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1366147232418004,"score_gpt":0.4752907634436966,"score_spread":0.3386760402018962,"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."}}