{"id":"W4404541836","doi":"10.34297/ajbsr.2024.21.002879","title":"Artificial Intelligence in Nuclear Medicine: Current Applications and Future Prospects","year":2024,"lang":"en","type":"article","venue":"American Journal of Biomedical Science & Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Current (fluid); Engineering ethics; Data science; Computer science; Engineering; Electrical engineering","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.006512862,0.00009732693,0.0003116423,0.001402898,0.0001773891,0.0001046285,0.0004072597,0.00003233079,0.0001139422],"category_scores_gemma":[0.0007833365,0.00006167349,0.00004469727,0.005015785,0.00944403,0.0001569925,0.0001240912,0.001820083,0.0000174673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002082257,"about_ca_system_score_gemma":0.0008249206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005989013,"about_ca_topic_score_gemma":0.000001227428,"domain_scores_codex":[0.996503,0.0001051783,0.0005194427,0.0003353405,0.002039016,0.000498005],"domain_scores_gemma":[0.9983699,0.0002928182,0.0000839432,0.0001628309,0.0002852711,0.0008051826],"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":[0.00003570086,0.0001044735,0.0002832464,0.00006169574,0.000006497465,0.0001517158,0.001145319,4.649007e-7,0.004962774,0.0105376,0.0004588069,0.9822517],"study_design_scores_gemma":[0.0004194642,0.005760543,0.01610748,0.002762783,0.00006164191,0.002857383,0.01443467,0.04623962,0.000240281,0.02250271,0.8882722,0.0003411887],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6651756,0.01513964,0.01654415,0.2982992,0.00160526,0.0009131783,0.00000309616,0.00007489436,0.002245011],"genre_scores_gemma":[0.9922835,0.003079701,0.002293893,0.000213415,0.002098278,0.000007178267,7.609435e-7,0.00001323539,0.00001009679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9819105,"threshold_uncertainty_score":0.9932517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03137257137755711,"score_gpt":0.4412815236806062,"score_spread":0.4099089523030491,"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."}}