{"id":"W4210430111","doi":"10.1007/s00345-022-03930-7","title":"Explainable artificial intelligence (XAI): closing the gap between image analysis and navigation in complex invasive diagnostic procedures","year":2022,"lang":"en","type":"review","venue":"World Journal of Urology","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Alberta","funders":"Westfälische Wilhelms-Universität Münster; Agence Nationale de la Recherche","keywords":"Cystoscopy; Medicine; Bladder cancer; Gold standard (test); General surgery; Surgery; Medical physics; Radiology; Urinary system; Cancer; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.001526007,0.0002341806,0.001439633,0.001357738,0.0002257982,0.00004452966,0.0002379414,0.0001345615,0.0003514622],"category_scores_gemma":[0.003341012,0.000167491,0.0003213102,0.002035248,0.0002275896,0.0001059895,0.00007528858,0.001307785,0.000008914866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002585483,"about_ca_system_score_gemma":0.0009602512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002470924,"about_ca_topic_score_gemma":0.0009892188,"domain_scores_codex":[0.9967758,0.0006717465,0.001681887,0.0002678565,0.0002866814,0.0003160465],"domain_scores_gemma":[0.9920759,0.006146814,0.001214311,0.0002222925,0.0002128204,0.0001278638],"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":[0.00005343521,0.00009048942,0.002956453,0.00279678,0.000386393,0.0001687368,0.001385918,0.00004478576,0.000002530197,0.0002521327,0.0002015409,0.9916608],"study_design_scores_gemma":[0.00009156418,0.002134809,0.00423703,0.007823863,0.02440255,0.002352922,0.006815852,0.000206027,0.0000759677,0.03131991,0.9197311,0.0008084509],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002814648,0.9919811,0.0006728164,0.003363932,0.0003009691,0.0007636229,0.00001128976,0.000007588275,0.00008396029],"genre_scores_gemma":[0.07767849,0.9208902,0.0002242767,0.0002594447,0.0007367227,0.00005177426,0.0001155714,0.00002320952,0.0000202957],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9908524,"threshold_uncertainty_score":0.6830083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2823793112158683,"score_gpt":0.4623704889361394,"score_spread":0.1799911777202711,"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."}}