{"id":"W4393065482","doi":"10.1053/j.semnuclmed.2024.02.005","title":"Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends","year":2024,"lang":"en","type":"review","venue":"Seminars in Nuclear Medicine","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Calgary","funders":"","keywords":"Workflow; Medicine; Positron emission tomography; Myocardial perfusion imaging; Correction for attenuation; Medical physics; Artificial intelligence; Image quality; Emission computed tomography; Medical imaging; Image registration; Computer science; Computer vision; Radiology; Image (mathematics); Perfusion; Database","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001872782,0.001084168,0.002159508,0.003689844,0.0002563078,0.002547023,0.001377868,0.002218652,0.004115388],"category_scores_gemma":[0.003033186,0.0004077451,0.0009552972,0.005057473,0.001058887,0.003344877,0.001212699,0.002948659,0.00191336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009524167,"about_ca_system_score_gemma":0.002447614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001358192,"about_ca_topic_score_gemma":0.003264863,"domain_scores_codex":[0.9994414,0.0001223261,0.0001162505,0.00008225555,0.0001918663,0.00004580661],"domain_scores_gemma":[0.9963951,0.002355369,0.0003734918,0.00005841365,0.0005915716,0.0002261848],"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":[0.000099359,0.0000847331,0.0003251955,0.02609399,0.000165792,0.0001820577,0.00008556349,0.0004107764,0.000513986,0.002243162,0.0460068,0.9237885],"study_design_scores_gemma":[0.00008853651,0.0002268599,0.002251473,0.0173368,0.0005342243,0.00192271,0.0002223192,0.0004965971,0.0002775843,0.003981496,0.9725801,0.00008129899],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005378431,0.9988663,0.00009830229,0.0004393456,0.0002672811,0.000002365341,0.00001122343,0.000006338516,0.0002550069],"genre_scores_gemma":[0.0003911715,0.9979774,0.0003343164,0.00053195,0.0005389421,0.000004308748,0.00002216538,0.000002133518,0.0001976956],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004115388,"threshold_uncertainty_score":0.01376742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03198618558937387,"score_gpt":0.3596589050062771,"score_spread":0.3276727194169032,"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."}}