{"id":"W4411242685","doi":"10.2196/77332","title":"Utility of Generative Artificial Intelligence for Japanese Medical Interview Training: Randomized Crossover Pilot Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Crossover; Crossover study; Randomized controlled trial; Artificial intelligence; Computer science; Psychology; Medicine; Alternative medicine; Placebo; World Wide Web","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":[0.01170577,0.001767682,0.002634344,0.00101843,0.001534841,0.00081211,0.001383368,0.001677652,0.005311185],"category_scores_gemma":[0.00869765,0.001080784,0.001284979,0.0006754897,0.001904726,0.001284,0.001103413,0.00183207,0.0006554904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001153185,"about_ca_system_score_gemma":0.002845384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001112906,"about_ca_topic_score_gemma":0.001773688,"domain_scores_codex":[0.9942286,0.003529565,0.0005001495,0.0006937435,0.0004935317,0.000554379],"domain_scores_gemma":[0.9923739,0.00274029,0.001041749,0.001299385,0.0009658166,0.001578835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"nonrandomized_trial","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.4015406,0.5301005,0.006301539,0.001355308,0.0005051776,0.0002181863,0.002648755,0.00130332,0.009234567,0.0004008691,0.0007119999,0.04567909],"study_design_scores_gemma":[0.134068,0.855643,0.006739603,0.00004356854,0.0002443162,0.00002537654,0.0003975295,0.0008621658,0.001036909,0.000150231,0.0007469736,0.00004237274],"study_design_candidate":"randomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700467,0.0001309746,0.001507668,0.00007310824,0.000123309,0.02755907,0.0001265977,0.00003624384,0.0003963416],"genre_scores_gemma":[0.9037561,0.0003853264,0.01766364,0.0003982621,0.000242202,0.07597984,0.0002629955,0.00001851413,0.00129312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01170577,"threshold_uncertainty_score":0.06190675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2738637925591789,"score_gpt":0.5231655511726371,"score_spread":0.2493017586134582,"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."}}