{"id":"W4402277801","doi":"10.2196/58478","title":"Practical Applications of Large Language Models for Health Care Professionals and Scientists","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biomedicine; Relevance (law); Exploit; Health care; Productivity; Narrative; Engineering ethics; Computer science; Knowledge management; Engineering; Political 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005788645,0.00006503346,0.0001716834,0.0001078922,0.00009625408,0.00001851139,0.00004603153,0.0001171586,0.0000667963],"category_scores_gemma":[0.0001871432,0.00004883738,0.00003964556,0.0002050722,0.00009047094,0.0001775318,0.00003245893,0.0002056362,0.00001515138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005849262,"about_ca_system_score_gemma":0.001432831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001669139,"about_ca_topic_score_gemma":0.00001609745,"domain_scores_codex":[0.9987283,0.00001604576,0.0005563805,0.00008379023,0.0004154586,0.000200039],"domain_scores_gemma":[0.9990506,0.0002694341,0.00008195965,0.0001393734,0.0001695717,0.0002891163],"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":[0.00008225346,0.0005537448,0.0001796436,0.02511423,0.00005385469,0.000005315595,0.2760562,0.000001554534,0.00001203433,0.09094794,0.1030743,0.5039189],"study_design_scores_gemma":[0.0005208566,0.001153667,0.0001802077,0.005291996,0.0001166867,0.0002876669,0.4451236,0.3474112,0.002208328,0.004994244,0.1923234,0.0003881843],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2116209,0.006943339,0.6903228,0.07894916,0.001082377,0.007446285,0.0003134423,0.0002898924,0.00303182],"genre_scores_gemma":[0.9788731,0.0001089402,0.01598866,0.003823439,0.0002520494,0.0005010477,0.000209226,0.00001097919,0.0002325354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7672522,"threshold_uncertainty_score":0.254178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1063619784051393,"score_gpt":0.550919791258643,"score_spread":0.4445578128535037,"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."}}