{"id":"W4401865852","doi":"10.20944/preprints202408.1702.v1","title":"A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Niagara; Brock University","funders":"","keywords":"Health care; Data science; Computer science; Artificial intelligence; Management science; Engineering; Political science","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003772288,0.0003434574,0.00062728,0.0003860146,0.00007428934,0.0000533127,0.001629923,0.0003940564,0.00002388473],"category_scores_gemma":[0.0005915224,0.0003814057,0.00007294732,0.0005523204,0.0001552358,0.00009267664,0.006309695,0.00183796,0.0001838841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001786584,"about_ca_system_score_gemma":0.000616608,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01413811,"about_ca_topic_score_gemma":0.002246866,"domain_scores_codex":[0.9954264,0.0009770239,0.001022703,0.001700205,0.0004216666,0.0004520414],"domain_scores_gemma":[0.9965835,0.0005504796,0.0004012809,0.001965406,0.0003203775,0.0001789265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002942526,0.0001805917,0.399136,0.009862703,0.00004696787,0.0000267483,0.006443528,0.001424757,0.00004342503,0.3482257,0.000006084465,0.2345741],"study_design_scores_gemma":[0.00007004624,0.00006794618,0.7245884,0.001194463,0.00001091505,0.00001497608,0.0002451802,0.04285879,0.001329484,0.2277685,0.00117678,0.0006745099],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6142258,0.1929185,0.08065102,0.09296531,0.003573359,0.009809017,0.0002247931,0.001763628,0.003868573],"genre_scores_gemma":[0.9900333,0.006603088,0.002448125,0.00008373202,0.00007268712,0.0006543199,0.00002160678,0.00003121909,0.00005193384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3758075,"threshold_uncertainty_score":0.9998638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2642097645256881,"score_gpt":0.4259579096495444,"score_spread":0.1617481451238564,"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."}}