{"id":"W4406567827","doi":"10.1016/b978-0-44-323761-4.00027-4","title":"Interpretable AI for medical image analysis: methods, evaluation, and clinical considerations","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Image (mathematics); Computer science; Pattern recognition (psychology)","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":[0.007219032,0.0003775312,0.0009583224,0.0005065638,0.0003125927,0.000472885,0.0007807673,0.0005250551,0.0005821942],"category_scores_gemma":[0.004249552,0.00036369,0.0005610102,0.00009596819,0.000371622,0.0002550671,0.0005853762,0.0006425675,0.00003603479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009757497,"about_ca_system_score_gemma":0.001460229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003366044,"about_ca_topic_score_gemma":0.0002873477,"domain_scores_codex":[0.995989,0.0004341309,0.001413311,0.001109039,0.0006982064,0.0003563445],"domain_scores_gemma":[0.993013,0.003875597,0.0003546725,0.001200852,0.001276307,0.0002796154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004402038,0.00001227051,0.000004767104,0.00002548316,0.0007276888,0.000008169429,0.0001504024,0.000002751698,0.000002305468,0.2379304,0.001772984,0.7593583],"study_design_scores_gemma":[0.0001747284,0.00007647444,0.000005081456,0.0001992067,0.001703913,0.000009108404,0.00001323315,0.1606741,0.0001154904,0.4521398,0.3845196,0.0003693173],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[8.260599e-7,0.0008972214,0.4806493,0.001554174,0.0006699135,0.0008127096,0.0000153592,0.00008164167,0.5153189],"genre_scores_gemma":[0.0002391215,0.0001547058,0.3353469,0.004027352,0.0002613066,0.0002508556,0.0000298038,0.00003245455,0.6596575],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.758989,"threshold_uncertainty_score":0.9998815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05121775129253286,"score_gpt":0.4308372453972434,"score_spread":0.3796194941047105,"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."}}