{"id":"W4399206797","doi":"10.2196/58491","title":"AI: Bridging Ancient Wisdom and Modern Innovation in Traditional Chinese Medicine","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Medicine; Computer science; Data science","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":[0.0008051204,0.0002423617,0.0004715708,0.0008074603,0.00006723846,0.00002385612,0.00008743665,0.000133138,0.0002324101],"category_scores_gemma":[0.0008808517,0.000158389,0.00004278207,0.001400545,0.0004117824,0.0003712631,0.00005461501,0.0007922731,0.00002446495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001309732,"about_ca_system_score_gemma":0.0002998048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001119987,"about_ca_topic_score_gemma":0.00001001526,"domain_scores_codex":[0.9969593,0.0000194306,0.001097723,0.0001753051,0.001460517,0.0002877608],"domain_scores_gemma":[0.9990549,0.000329829,0.00007666429,0.0001521883,0.0001354023,0.0002509789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0005685527,0.001280353,0.09703798,0.01542208,0.0007227912,0.002274047,0.1337511,0.00009045241,0.001563594,0.1712222,0.2888422,0.2872247],"study_design_scores_gemma":[0.006473269,0.0007774513,0.4722748,0.00706693,0.00009924641,0.002767053,0.001811179,0.4640884,0.00001902885,0.02697944,0.01715956,0.000483559],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9494706,0.00131031,0.004734979,0.03661713,0.0005884756,0.0004934815,0.00001978606,0.0002016276,0.006563567],"genre_scores_gemma":[0.9855254,0.0001378038,0.0005021025,0.01211013,0.001295348,0.0000856215,0.0002470213,0.00002069399,0.00007587141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.463998,"threshold_uncertainty_score":0.6458915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03125304149741721,"score_gpt":0.3299777781991348,"score_spread":0.2987247367017176,"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."}}