{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1628048,0.0006445499,0.000781845,0.001347585,0.003491977,0.01185711,0.002577668,0.004854714,0.006679031],"category_scores_gemma":[0.4022304,0.0007711536,0.0007956958,0.001114695,0.01604341,0.01801203,0.01663827,0.008473529,0.000837807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006171246,"about_ca_system_score_gemma":0.01141551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003373416,"about_ca_topic_score_gemma":0.003501691,"domain_scores_codex":[0.8210702,0.1522025,0.004712898,0.004261459,0.01449985,0.003253218],"domain_scores_gemma":[0.6208346,0.3304579,0.01154568,0.02009263,0.01247028,0.004598917],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002684318,0.0001964436,0.01511991,0.0003884768,0.00009738845,0.000303758,0.0199809,0.005709714,0.001349844,0.8698167,0.005479002,0.08128943],"study_design_scores_gemma":[0.0001052562,0.0001609166,0.003063313,0.001061548,0.00005588871,0.0003842931,0.007483324,0.02116306,0.001655651,0.9363527,0.02843485,0.00007915875],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.1056543,0.003414356,0.442012,0.34431,0.0005610228,0.001009866,0.0002302016,0.0004089414,0.1023993],"genre_scores_gemma":[0.8729984,0.0007356422,0.1123216,0.01067766,0.000315163,0.0009913724,0.00006854294,0.0001764696,0.001715058],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.8371952,"threshold_uncertainty_score":0.8610044,"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."}}