{"id":"W3185745426","doi":"10.1016/j.cjca.2021.07.016","title":"The Role of Artificial Intelligence and Machine Learning in Clinical Cardiac Electrophysiology","year":2021,"lang":"en","type":"review","venue":"Canadian Journal of Cardiology","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto General Hospital; University Health Network","funders":"","keywords":"Medicine; Artificial intelligence; Cardiac electrophysiology; Rigour; Atrial fibrillation; Deep learning; Clinical Practice; Cardiac resynchronization therapy; Machine learning; Cardiac arrhythmia; Data science; Computer science; Cardiology; Internal medicine; Heart failure; Electrophysiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002338401,0.0009582644,0.002108338,0.003511423,0.0003589895,0.002106818,0.001350786,0.002155086,0.004096848],"category_scores_gemma":[0.005006311,0.0003155644,0.0008210115,0.003656369,0.001411739,0.002341524,0.001018131,0.003226681,0.001146431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001439281,"about_ca_system_score_gemma":0.00316351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004723863,"about_ca_topic_score_gemma":0.006876837,"domain_scores_codex":[0.9993623,0.0001577829,0.00009926465,0.00009757837,0.0002463339,0.00003669505],"domain_scores_gemma":[0.9941866,0.004292993,0.0003375571,0.00008407903,0.0009164943,0.0001822799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006178391,0.00007062313,0.0003153061,0.02217518,0.0001859073,0.000138938,0.00005937905,0.0005206,0.0003490949,0.007129997,0.02474748,0.9442457],"study_design_scores_gemma":[0.00006326542,0.0001559728,0.002850869,0.02121086,0.0004827847,0.001176103,0.0001644414,0.0006655176,0.0002851127,0.01177262,0.9610959,0.00007662056],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0000345296,0.9988768,0.0001281252,0.0004398873,0.0001536356,0.000002054556,0.000007615567,0.000003047173,0.0003542928],"genre_scores_gemma":[0.0004409138,0.9984564,0.0002642681,0.0003113952,0.0003682032,0.000003152519,0.00001154984,0.000001159444,0.0001430817],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004723863,"threshold_uncertainty_score":0.01370525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05844258223420159,"score_gpt":0.3551005924027764,"score_spread":0.2966580101685748,"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."}}