{"id":"W3213384164","doi":"10.1016/j.inffus.2021.10.007","title":"Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence","year":2021,"lang":"en","type":"article","venue":"Information Fusion","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":209,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Discovery Centre; University Health Network; University of Alberta","funders":"Eusko Jaurlaritza; Bundesministerium für Bildung und Forschung; Canadian Institutes of Health Research; Austrian Science Fund; Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Computer science; Workflow; Artificial intelligence; Context (archaeology); Process (computing); Data science; Enabling; Inference","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06427458,0.001485014,0.002227089,0.006085298,0.002521897,0.01165143,0.004361477,0.004959101,0.003444612],"category_scores_gemma":[0.05358101,0.001265987,0.002272792,0.003008142,0.01779909,0.02388036,0.01659249,0.007318004,0.001000979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004020782,"about_ca_system_score_gemma":0.006409163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001293702,"about_ca_topic_score_gemma":0.0009918795,"domain_scores_codex":[0.9719282,0.01672346,0.001620175,0.002262643,0.00644439,0.001021107],"domain_scores_gemma":[0.9429867,0.03664171,0.003218755,0.01118034,0.004748549,0.001223925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004747251,0.00004162409,0.0005452229,0.0003071096,0.0001120755,0.0001451522,0.001008021,0.008358756,0.0007184064,0.9421825,0.001786507,0.04474711],"study_design_scores_gemma":[0.00001342533,0.00003233569,0.0001385658,0.0002357346,0.00003543813,0.00007066194,0.0002059055,0.01419992,0.0008708908,0.9718484,0.01231703,0.00003167641],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006538029,0.005842768,0.9420583,0.02788281,0.0004021248,0.0001843896,0.0001054515,0.0003879993,0.0165981],"genre_scores_gemma":[0.3724504,0.009881447,0.6084452,0.004017362,0.00130584,0.0006235768,0.0003298015,0.0002278179,0.002718622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06427458,"threshold_uncertainty_score":0.3399205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0660629738283136,"score_gpt":0.3969621407040641,"score_spread":0.3308991668757505,"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."}}