{"id":"W4313591971","doi":"10.1016/j.compmedimag.2022.102171","title":"Towards better interpretable and generalizable AD detection using collective artificial intelligence","year":2023,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Centre National de la Recherche Scientifique; Eisai; Agence Nationale de la Recherche; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Interpretability; Artificial intelligence; Computer science; Machine learning; Deep learning; Generalizability theory; Neuroimaging; Convolutional neural network; Classifier (UML); Ensemble learning; Dementia; Graph; Disease; Medicine; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005850298,0.0001401597,0.0002473301,0.0003401187,0.0002205119,0.0001040055,0.00006199316,0.00007245667,0.00005315071],"category_scores_gemma":[0.000168181,0.0001218303,0.00005414354,0.0006878399,0.0003475063,0.00009832861,0.000207511,0.0003029838,0.000005273542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002912676,"about_ca_system_score_gemma":0.0001425582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005951548,"about_ca_topic_score_gemma":0.000005146922,"domain_scores_codex":[0.998541,0.0000907112,0.0002305977,0.0003379865,0.0004475883,0.0003521479],"domain_scores_gemma":[0.9993307,0.00008986457,0.00003436609,0.0001044781,0.0001324372,0.0003081712],"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":[0.0003160723,0.0001422368,0.01614016,0.000249194,0.0001670494,0.0004118798,0.000717189,0.000002889081,0.0188621,0.000218788,0.0003810439,0.9623914],"study_design_scores_gemma":[0.0007161243,0.0002015727,0.02009928,0.0003328156,0.00009495753,0.0003159141,0.0002287221,0.9634523,0.005318216,0.007850541,0.001198897,0.0001906959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.696389,0.0002858833,0.3003154,0.002322929,0.0002401041,0.0002095004,0.000002644927,0.0001076073,0.000126923],"genre_scores_gemma":[0.9948794,0.0006616972,0.002528933,0.001614853,0.0001689543,0.00001893466,0.00001441937,0.00001880889,0.00009405149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9634494,"threshold_uncertainty_score":0.4968095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03706426654043213,"score_gpt":0.3408039305231152,"score_spread":0.303739663982683,"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."}}