{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003526974,0.00008155742,0.0001058126,0.0001911271,0.00003442617,0.000110149,0.0003602732,0.00004975134,0.000002936537],"category_scores_gemma":[0.00002255965,0.00006927131,0.00003155844,0.0003699836,0.000008279148,0.001381924,0.0001040889,0.000081901,0.00001622651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007634684,"about_ca_system_score_gemma":0.00008425178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002814985,"about_ca_topic_score_gemma":0.00002582524,"domain_scores_codex":[0.9990956,0.000007170052,0.0003043522,0.0001278335,0.0001799875,0.0002851204],"domain_scores_gemma":[0.9995627,0.00005916287,0.00003266408,0.000248855,0.00003492627,0.0000617101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001546228,0.0000120712,0.000007640286,0.0005624819,0.000003033309,0.000004865265,0.01332004,0.0006483631,0.000005511537,0.1836164,0.0005401167,0.8012779],"study_design_scores_gemma":[0.0001256168,0.00002060628,0.000001887476,0.00008122325,8.237658e-7,0.000003172847,0.0004732728,0.8234358,0.00004072897,0.008440369,0.167303,0.00007343783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007117168,0.01682212,0.9778675,0.002306885,0.000925619,0.0002560015,0.00002637048,0.0001950033,0.0008887293],"genre_scores_gemma":[0.4483656,0.01260572,0.5357007,0.002263386,0.0003991357,0.0001986251,0.00007788713,0.00002993972,0.000359007],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8227875,"threshold_uncertainty_score":0.2824802,"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."}}