{"id":"W2913985588","doi":"10.2967/jnumed.118.223495","title":"Machine Learning in Nuclear Medicine: Part 1—Introduction","year":2019,"lang":"en","type":"review","venue":"Journal of Nuclear Medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Health Sciences Centre; Montreal Neurological Institute and Hospital; University of Toronto; Sunnybrook Health Science Centre; University of Waterloo; University of British Columbia; McGill University","funders":"Society of Nuclear Medicine and Molecular Imaging","keywords":"Context (archaeology); Field (mathematics); Computer science; Artificial intelligence; Machine learning; Data science; Medical physics; Medicine; History; Mathematics","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":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003838772,0.0007215467,0.00557932,0.001738822,0.000111807,0.00002487875,0.0005381451,0.0005157561,0.004294192],"category_scores_gemma":[0.004857194,0.0004559742,0.0006910061,0.0009504333,0.0005036264,0.0001800375,0.0001197479,0.006293191,0.0002373255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005195008,"about_ca_system_score_gemma":0.0003302381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006902341,"about_ca_topic_score_gemma":0.000001100747,"domain_scores_codex":[0.9939134,0.0005700969,0.002915545,0.0005827011,0.001414084,0.0006042158],"domain_scores_gemma":[0.9955536,0.0004585248,0.002502486,0.0005989746,0.0003017799,0.0005846451],"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":[0.0001921156,0.0001611799,0.0000645303,0.01196254,0.000490771,0.00103235,0.0005999889,0.00002318061,0.00002770998,0.000380752,0.1709527,0.8141122],"study_design_scores_gemma":[0.003210381,0.002150535,0.0000362396,0.05328208,0.0028168,0.006987758,0.0002516943,0.0007317246,3.555564e-8,0.00002893656,0.9301915,0.0003123599],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004226663,0.9578702,0.00001436816,0.0327877,0.003779822,0.0005308752,0.000001796039,0.00006829746,0.004904613],"genre_scores_gemma":[0.00006086981,0.9816027,0.0003944923,0.001971294,0.01386692,0.000001946701,0.00005914653,0.0002856683,0.001756951],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8137999,"threshold_uncertainty_score":0.9997892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03646419099408104,"score_gpt":0.347644446213445,"score_spread":0.3111802552193639,"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."}}