{"id":"W4399542284","doi":"10.2196/56117","title":"A Use Case for Generative AI in Medical Education","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Generative grammar; Computer science; Artificial intelligence; Natural language processing","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007493673,0.0001534832,0.000217671,0.0003690725,0.00009283754,0.0000822137,0.00008796935,0.0004063452,0.001521903],"category_scores_gemma":[0.005292676,0.000133619,0.0000879033,0.000573426,0.0001062885,0.000312857,0.0000170774,0.0005538479,0.0001132725],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004472765,"about_ca_system_score_gemma":0.02469629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00177575,"about_ca_topic_score_gemma":0.001070766,"domain_scores_codex":[0.9979451,0.0000943417,0.0006252482,0.0004250748,0.0006124976,0.0002976927],"domain_scores_gemma":[0.9981841,0.0004226184,0.00004911464,0.0002410904,0.0003634031,0.0007396307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007802382,0.001499658,0.002247479,0.0005280224,0.00001677558,0.0001227016,0.005833513,4.295723e-7,0.00004894042,0.007263164,0.1559916,0.8263698],"study_design_scores_gemma":[0.0004749632,0.001143515,0.007485334,0.008543821,0.000216384,0.01268542,0.02093606,0.08995482,0.002520135,0.0240915,0.8310069,0.0009411722],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7989171,0.001496823,0.001558798,0.1898995,0.005848977,0.001725915,0.000004013901,0.0001131331,0.0004357083],"genre_scores_gemma":[0.9627433,0.0002050406,0.0009980458,0.02783176,0.003515134,0.002244836,0.0002914971,0.0000338662,0.002136452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8254285,"threshold_uncertainty_score":0.9993908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.139294354644328,"score_gpt":0.5354344051062581,"score_spread":0.3961400504619301,"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."}}