{"id":"W4408219806","doi":"10.1186/s12911-025-02891-2","title":"On the practical, ethical, and legal necessity of clinical Artificial Intelligence explainability: an examination of key arguments","year":2025,"lang":"en","type":"review","venue":"BMC Medical Informatics and Decision Making","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of British Columbia","funders":"","keywords":"Key (lock); Health informatics; Engineering ethics; Computer science; Medicine; Nursing; Computer security; Public health; Engineering","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01004942,0.0002308771,0.001377868,0.0002489499,0.0001598776,0.00006514003,0.0001989702,0.0009865948,0.0001138678],"category_scores_gemma":[0.04174313,0.0001373419,0.0001908651,0.000381663,0.0005436254,0.0001578925,0.0001936483,0.001467374,0.000005506241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004033352,"about_ca_system_score_gemma":0.002065296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001763741,"about_ca_topic_score_gemma":0.0000249833,"domain_scores_codex":[0.9940328,0.0006416546,0.003715131,0.0002577716,0.001154819,0.0001978029],"domain_scores_gemma":[0.9650565,0.03255764,0.001205519,0.0005203704,0.0003898642,0.0002700827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001029393,0.00021749,0.00002076432,0.01169622,0.00002108477,0.000002393312,0.0003703603,9.934241e-7,7.846674e-9,0.02129057,0.0001177832,0.9661594],"study_design_scores_gemma":[0.0003675728,0.006313676,0.0004768512,0.3665017,0.002947745,0.0004062249,0.03089648,0.2697509,0.00005414804,0.1486244,0.1722497,0.001410681],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.09688973,0.3637644,0.5299222,0.001086637,0.002148414,0.004206868,0.0000385542,0.00005652263,0.001886581],"genre_scores_gemma":[0.01911716,0.9543836,0.02520286,0.001031324,0.0001832263,0.00003702689,0.00002525154,0.00001210132,0.000007413982],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9647487,"threshold_uncertainty_score":0.9663287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4485012631719023,"score_gpt":0.5920232282053801,"score_spread":0.1435219650334779,"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."}}