{"id":"W2955164461","doi":"10.1007/s00259-019-04395-4","title":"EJNMMI supplement: bringing AI and radiomics to nuclear medicine","year":2019,"lang":"en","type":"editorial","venue":"European Journal of Nuclear Medicine and Molecular Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Women's College Hospital; University of Toronto","funders":"","keywords":"Radiomics; Medicine; Medical physics; Nuclear medicine; Radiology","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"],"consensus_categories":[],"category_scores_codex":[0.003576339,0.0006548794,0.001677342,0.001073759,0.0001929306,0.0001239471,0.0004655616,0.0001285105,0.0002283288],"category_scores_gemma":[0.004037696,0.0005197769,0.0001999982,0.000284951,0.0005414069,0.0001723495,0.0004041308,0.003019664,0.00002940533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001415103,"about_ca_system_score_gemma":0.0001491755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005342351,"about_ca_topic_score_gemma":2.493541e-7,"domain_scores_codex":[0.9953575,0.0004132177,0.001401952,0.000693057,0.00149128,0.0006429738],"domain_scores_gemma":[0.9967001,0.0003113125,0.0008108484,0.0005373142,0.0005265801,0.001113816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002431699,0.00003116178,0.00008183582,0.0004840223,0.0002846209,0.004710472,0.002580779,0.000006354838,0.01109447,0.0000923664,0.9590902,0.02130052],"study_design_scores_gemma":[0.006123709,0.001484284,0.0001185556,0.006280505,0.001059821,0.00210589,0.0009748242,0.001621763,0.000009609234,0.00002163018,0.9797673,0.0004321386],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"editorial","genre_scores_codex":[0.01205075,0.02044138,0.006289557,0.4691264,0.4682591,0.001370234,0.00003486958,0.0002270103,0.02220072],"genre_scores_gemma":[0.07724084,0.01031442,0.01255387,0.1243127,0.773464,0.00000146584,0.0001633374,0.001433134,0.0005162803],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.3448138,"threshold_uncertainty_score":0.9997254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006140260064794959,"score_gpt":0.2749790751018757,"score_spread":0.2688388150370807,"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."}}