{"id":"W2974630971","doi":"10.21037/jmai.2019.09.04","title":"Artificial intelligence and colorectal polyp detection","year":2019,"lang":"en","type":"article","venue":"Journal of Medical Artificial Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"","keywords":"Colorectal cancer; Colonoscopy; Incidence (geometry); Medicine; Cancer; Cause of death; Internal medicine; Oncology; General surgery; Disease","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002491208,0.0002005239,0.0005521629,0.000292091,0.00009654323,0.00007996229,0.0003036859,0.0002558574,0.001251343],"category_scores_gemma":[0.00315924,0.0001547319,0.0001881535,0.0003788859,0.000386127,0.0001632078,0.00009887455,0.001553071,0.000158455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008182642,"about_ca_system_score_gemma":0.0002910991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006613024,"about_ca_topic_score_gemma":0.00001726398,"domain_scores_codex":[0.9964459,0.000116387,0.001219142,0.0002823739,0.001570109,0.0003660927],"domain_scores_gemma":[0.998032,0.0004571336,0.0003831467,0.0001692293,0.0002540701,0.000704388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005808212,0.0002646247,0.001801748,0.00007310198,0.00007215658,0.0002960274,0.0004205156,0.0001204346,0.01612269,0.007306185,0.00005135003,0.9728903],"study_design_scores_gemma":[0.0003159368,0.006140815,0.004463661,0.001779632,0.0003835228,0.01050672,0.004005126,0.5681772,0.3252408,0.07175095,0.00641401,0.0008216518],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8253472,0.0004291079,0.1605673,0.01141995,0.001652353,0.0001940994,7.453557e-7,0.00002820434,0.0003610656],"genre_scores_gemma":[0.9958224,0.0004273437,0.001667165,0.0008222405,0.00118383,0.000002011689,0.000001134906,0.00002444567,0.00004939858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9720687,"threshold_uncertainty_score":0.9996616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02852518578460813,"score_gpt":0.3307299346638586,"score_spread":0.3022047488792505,"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."}}