{"id":"W7140608172","doi":"10.1109/3ict68299.2025.11442144","title":"Towards Transparent Diagnostics: Explainable AI Across Biomedical Signals","year":2025,"lang":"","type":"article","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature (linguistics); Identification (biology); Key (lock); Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":true,"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","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002522857,0.0009766397,0.001176008,0.0005571795,0.001487174,0.002200361,0.004575751,0.000702775,0.002624496],"category_scores_gemma":[0.001076983,0.0009524609,0.0005499982,0.004385345,0.001272736,0.001907694,0.001634534,0.001044338,0.001853635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005814119,"about_ca_system_score_gemma":0.002021882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00242728,"about_ca_topic_score_gemma":0.0004495371,"domain_scores_codex":[0.9901556,0.0004937119,0.002201806,0.002314638,0.001648414,0.003185879],"domain_scores_gemma":[0.9942268,0.001301615,0.0002563349,0.002247599,0.001001047,0.0009666411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001340119,0.003058551,0.0006208255,0.0006722104,0.0004912464,0.0009769765,0.01521771,0.002802062,0.001783606,0.3220841,0.06848254,0.5836762],"study_design_scores_gemma":[0.001082749,0.001328055,0.0006733442,0.00133475,0.0002071439,0.00003219414,0.007531156,0.3587703,0.2828308,0.07405096,0.2700748,0.002083752],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005174065,0.004231493,0.9303476,0.03593544,0.00546708,0.001156117,0.00006865619,0.0004777471,0.01714183],"genre_scores_gemma":[0.9637146,0.002802091,0.007248216,0.01071886,0.0004374764,0.0002785264,0.00001852314,0.00005307351,0.01472863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9585406,"threshold_uncertainty_score":0.9998128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04544530675980647,"score_gpt":0.3596954544006999,"score_spread":0.3142501476408934,"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."}}