{"id":"W7093362435","doi":"10.2196/79039","title":"Large Language Model–Based Virtual Patient Systems for History-Taking in Medical Education: Comprehensive Systematic Review","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Unified Medical Language System; Virtual patient; Health informatics; MEDLINE; Representation (politics); Telemedicine; Systematized Nomenclature of Medicine; Disease; Semantics (computer science)","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":[0.01438953,0.00138557,0.00654209,0.006736749,0.0005416963,0.002548277,0.002440706,0.001492758,0.007352874],"category_scores_gemma":[0.06298514,0.0009359617,0.01043475,0.005740006,0.000835585,0.003035225,0.002087655,0.001363133,0.0005588026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003395249,"about_ca_system_score_gemma":0.01121849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008187786,"about_ca_topic_score_gemma":0.01958247,"domain_scores_codex":[0.9910245,0.004767983,0.00240676,0.000558205,0.001070929,0.0001716329],"domain_scores_gemma":[0.9456703,0.04566679,0.005228447,0.0009241973,0.002199139,0.0003110661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0004503348,0.00004660654,0.000496825,0.8954667,0.009850002,0.00003989838,0.0001810349,0.0003939388,0.00009574812,0.0003101821,0.001383054,0.09128576],"study_design_scores_gemma":[0.0008521291,0.0007131418,0.002917919,0.8820664,0.09120073,0.0001971719,0.0003373298,0.0006672523,0.0002740122,0.0009681461,0.01971824,0.00008752503],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001424465,0.9957259,0.0008386279,0.0003326491,0.0000717821,0.0006637133,0.0005815865,0.00004173383,0.0003194811],"genre_scores_gemma":[0.03409752,0.95794,0.004531219,0.0007124072,0.00008037267,0.00179395,0.0007041342,0.00002331213,0.000117093],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01438953,"threshold_uncertainty_score":0.07609999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01912062361534121,"score_gpt":0.3483418598075553,"score_spread":0.3292212361922141,"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."}}