{"id":"W4392807024","doi":"10.3390/biomedinformatics4010047","title":"Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"AI in Service Interactions","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; Toronto Metropolitan University; University Health Network","funders":"Canadian Institutes of Health Research; York University","keywords":"Generative grammar; Transformer; Computer science; Health care; Data science; Artificial intelligence; Engineering; Political science; Electrical 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":[0.0004377368,0.000227188,0.0002471636,0.0006809866,0.0001482628,0.0001667205,0.0004109659,0.000187065,0.00006422817],"category_scores_gemma":[0.00002837085,0.0001955792,0.00007255386,0.0008443633,0.00005315301,0.001550429,0.00009107025,0.0004682769,0.00002182228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001792067,"about_ca_system_score_gemma":0.0003052964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003338079,"about_ca_topic_score_gemma":0.000954694,"domain_scores_codex":[0.9981276,0.00005689409,0.0005776282,0.0002876769,0.0005403527,0.0004098237],"domain_scores_gemma":[0.9991248,0.0001184853,0.00007675566,0.0003272293,0.00008520916,0.0002675112],"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":[0.000008101676,0.0001427205,0.000006575475,0.0006483207,0.00005657428,0.000018739,0.26285,0.00001602732,0.00002314164,0.05584855,0.001184643,0.6791967],"study_design_scores_gemma":[0.0005502098,0.00006964817,0.0002766906,0.0002773784,0.00001276214,0.00005299237,0.006713393,0.8620119,0.00003791208,0.0007303653,0.129024,0.0002426738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01823849,0.1542199,0.5127724,0.2874102,0.0138525,0.002034954,0.0008973873,0.002524101,0.008050006],"genre_scores_gemma":[0.8503752,0.1045502,0.0383657,0.002427149,0.002023141,0.0007140979,0.0007624986,0.00009045516,0.0006915071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8619959,"threshold_uncertainty_score":0.7975487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02195279692973618,"score_gpt":0.3209466085783576,"score_spread":0.2989938116486214,"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."}}