{"id":"W4206757132","doi":"10.1007/s12149-021-01708-2","title":"The application of artificial intelligence in nuclear cardiology","year":2022,"lang":"en","type":"review","venue":"Annals of Nuclear Medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Medicine; Nuclear medicine; Cardiology; Artificial intelligence; Internal medicine; Medical physics; Interpretation (philosophy); Radiology; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00198872,0.0002293494,0.001974037,0.0003087469,0.00008950021,0.000003539033,0.0004488347,0.0001657277,0.000354661],"category_scores_gemma":[0.001613015,0.0001445873,0.0003341892,0.00058111,0.0007209042,0.00001878888,0.000151343,0.001073028,0.00002163377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003702381,"about_ca_system_score_gemma":0.0001087842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001512579,"about_ca_topic_score_gemma":0.000001937103,"domain_scores_codex":[0.9974059,0.000292556,0.001250845,0.0003194811,0.0004533914,0.0002777609],"domain_scores_gemma":[0.9978005,0.0007343384,0.0006743027,0.0006006295,0.00008101632,0.0001091779],"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":[0.00004899463,0.00004516989,0.000004082316,0.002242558,0.0001215684,0.00002595442,0.000181425,0.000006583885,0.000008557879,0.008517464,0.002173819,0.9866238],"study_design_scores_gemma":[0.00007955938,0.0004190831,0.00002446048,0.002761501,0.0002705603,0.00009671757,0.000333941,0.001202457,2.650646e-7,0.0005469027,0.9941668,0.00009778844],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004612287,0.9895616,0.0001172645,0.005646196,0.00027233,0.0006285998,0.000005734434,0.00001796727,0.003704201],"genre_scores_gemma":[0.0007764831,0.9980395,0.0001261417,0.0005404634,0.0003671413,0.00001932338,0.0000397791,0.00006587891,0.00002524365],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.991993,"threshold_uncertainty_score":0.58961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1160465990797037,"score_gpt":0.4279545826597412,"score_spread":0.3119079835800375,"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."}}