{"id":"W4394877296","doi":"10.3390/biomedinformatics4020062","title":"Recent Advances in Large Language Models for Healthcare","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Topic Modeling","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"New Brunswick Innovation Foundation; Fondation de la recherche en santé du Nouveau-Brunswick","keywords":"Field (mathematics); Health care; Variety (cybernetics); Computer science; Domain (mathematical analysis); Data science; Medical care; Management science; Risk analysis (engineering); Medicine; Political science; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006531506,0.001228212,0.00142322,0.002547338,0.000436481,0.003739378,0.002293249,0.001814955,0.006289649],"category_scores_gemma":[0.02378597,0.0008332474,0.001841284,0.003368034,0.001243627,0.00515189,0.002223439,0.004004367,0.003815489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0025859,"about_ca_system_score_gemma":0.002598318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007310065,"about_ca_topic_score_gemma":0.005904997,"domain_scores_codex":[0.9965701,0.001912436,0.0002464075,0.0005391224,0.0006284353,0.0001034758],"domain_scores_gemma":[0.9826533,0.0140122,0.0004569448,0.001278654,0.001313753,0.0002850925],"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":[0.0002763534,0.0001535384,0.003186113,0.00186852,0.0004398314,0.0002617349,0.0004661146,0.1326991,0.001683231,0.1596251,0.05411692,0.6452234],"study_design_scores_gemma":[0.00003472249,0.00007121005,0.0009787729,0.0005071525,0.0001287265,0.0002554017,0.0001070912,0.5925497,0.001072708,0.2542858,0.1499074,0.0001012416],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.005617961,0.09547015,0.8484755,0.03251505,0.001553631,0.0001201905,0.002006729,0.003583081,0.01065773],"genre_scores_gemma":[0.2340622,0.1612904,0.5610522,0.009658058,0.009809866,0.0006085376,0.008470075,0.001676908,0.01337174],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.007310065,"threshold_uncertainty_score":0.03454232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03239555851291891,"score_gpt":0.3223995153578027,"score_spread":0.2900039568448838,"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."}}