{"id":"W4390347789","doi":"10.2196/51199","title":"Empathy and Equity: Key Considerations for Large Language Model Adoption in Health Care","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Cancer Institute; National Institutes of Health","keywords":"Empathy; Equity (law); Health care; Key (lock); Software deployment; Psychology; Public relations; Political science; Social psychology; Economic growth; Economics; Computer science; Computer security","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":[0.0006845849,0.00008016075,0.0001625703,0.000212943,0.0001460025,0.00001971786,0.00003124456,0.0001402408,0.0001005612],"category_scores_gemma":[0.001699811,0.00007828479,0.00003016673,0.0002631871,0.00003048543,0.00008440737,0.00002347966,0.0001789967,0.00002793313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002248274,"about_ca_system_score_gemma":0.003859125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000374932,"about_ca_topic_score_gemma":0.000987403,"domain_scores_codex":[0.9988037,0.00004708536,0.000386141,0.0002207298,0.0002653551,0.000277017],"domain_scores_gemma":[0.9991525,0.0001730957,0.00006745554,0.0001464759,0.0001553667,0.0003051351],"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":[0.0001307642,0.001054889,0.01102875,0.002604995,0.0000136203,0.000005488171,0.3744985,0.00005346166,0.0001360619,0.02936917,0.08157341,0.4995309],"study_design_scores_gemma":[0.001491573,0.001294657,0.05470814,0.002739469,0.00006549793,0.0000988941,0.6223833,0.2481558,0.00077092,0.0492603,0.01831927,0.0007122394],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9261683,0.00099778,0.001054975,0.06920481,0.0005801487,0.001474955,0.00001877696,0.0001112633,0.0003890312],"genre_scores_gemma":[0.9895546,0.0002360844,0.00122395,0.007179826,0.0004051613,0.0006424656,0.0004566636,0.00001427856,0.0002869341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4988187,"threshold_uncertainty_score":0.6845922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1488404852371167,"score_gpt":0.5199966882629056,"score_spread":0.3711562030257889,"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."}}