{"id":"W4412523035","doi":"10.1053/j.semnuclmed.2025.06.012","title":"Artificial Intelligence for Tumor [18F]FDG PET Imaging: Advancements and Future Trends - Part II","year":2025,"lang":"en","type":"review","venue":"Seminars in Nuclear Medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"Medicine; Generalizability theory; Medical physics; Positron emission tomography; Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001118071,0.0006380565,0.002494578,0.000925224,0.0002191027,0.00002479334,0.0003344268,0.0001342897,0.0005305429],"category_scores_gemma":[0.001090452,0.0004875392,0.0002740524,0.0008228536,0.000480088,0.00007157309,0.000229677,0.001336017,0.000007775118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001981637,"about_ca_system_score_gemma":0.0001857052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000160811,"about_ca_topic_score_gemma":0.000002331637,"domain_scores_codex":[0.9966172,0.0001057848,0.001287297,0.000908457,0.0004569481,0.0006243165],"domain_scores_gemma":[0.998243,0.0004050849,0.0003957463,0.0005573195,0.00007673375,0.0003221],"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":[0.00007488172,0.0001107136,0.00001520114,0.01194988,0.00007372849,0.000394943,0.0002694116,3.415887e-7,0.00000146874,0.001209782,0.03054981,0.9553499],"study_design_scores_gemma":[0.0004404987,0.0004239311,0.000004227583,0.06308415,0.001042916,0.0006437917,0.00044383,0.0009490653,3.544744e-7,0.0003990418,0.9322307,0.0003374739],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002357676,0.9820417,0.00008538841,0.01085988,0.001962687,0.000935306,0.00003510046,0.00008779402,0.003968564],"genre_scores_gemma":[0.00001808304,0.9893596,0.002863878,0.001904292,0.002727236,0.0001027409,0.0003402343,0.0001215643,0.002562338],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9550124,"threshold_uncertainty_score":0.9997576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02248319376095519,"score_gpt":0.3682824389657958,"score_spread":0.3457992452048406,"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."}}