{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001433877,0.0001658953,0.003000469,0.000332932,0.00007048571,0.00001074605,0.0001534208,0.0003396954,0.000003233583],"category_scores_gemma":[0.0013638,0.0001092815,0.0009982093,0.0002834336,0.0003062979,0.00001151142,0.00002098858,0.001553673,0.000001634974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009874238,"about_ca_system_score_gemma":0.002523632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005835301,"about_ca_topic_score_gemma":0.0005286024,"domain_scores_codex":[0.9970235,0.001208127,0.0012086,0.0001787494,0.00008096351,0.0003000853],"domain_scores_gemma":[0.9980047,0.0007830176,0.0005207907,0.000192973,0.0001976659,0.0003008023],"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":[0.0000104467,0.000001421333,0.004500019,0.0001653353,0.000733444,0.0001610144,0.00001361412,0.0000383472,0.000003340356,0.0001254894,0.00002665838,0.9942209],"study_design_scores_gemma":[0.00003797823,0.0003669641,0.0002914685,0.001375371,0.001273117,0.0004291398,0.0001295501,0.00002015504,0.000003358756,0.0001625966,0.9958225,0.00008779301],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005110002,0.9985715,0.00006905141,0.00007211869,0.0005072378,0.00007027309,0.000006336966,0.000001292241,0.0001911911],"genre_scores_gemma":[0.01678221,0.981362,0.00005117456,0.000006503069,0.001730686,0.000002335271,0.00001135695,0.00001665048,0.00003712282],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9957958,"threshold_uncertainty_score":0.6750022,"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."}}