{"id":"W4411893832","doi":"10.1111/jgh.17040","title":"Impact of Introducing Artificial Intelligence on Colonoscopy: A Retrospective Study on Potential Benefits and Drawbacks","year":2025,"lang":"en","type":"article","venue":"Journal of Gastroenterology and Hepatology","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Medicine; Colonoscopy; CAD; Retrospective cohort study; Endoscopy; Internal medicine; Adenoma; Gastroenterology; Surgery; Colorectal cancer; Cancer","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":[],"consensus_categories":[],"category_scores_codex":[0.0003172412,0.0001207013,0.0005723989,0.0004074301,0.00006762166,0.000007313153,0.00004643753,0.0001003813,0.00001214111],"category_scores_gemma":[0.0001529725,0.00009427177,0.00009615299,0.0001070525,0.0001595149,0.00003240342,0.00003675856,0.0004454661,4.139591e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009396972,"about_ca_system_score_gemma":0.00007126644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005296215,"about_ca_topic_score_gemma":0.0001394196,"domain_scores_codex":[0.9989738,0.0001389124,0.0004158315,0.0002010878,0.000099456,0.0001709316],"domain_scores_gemma":[0.9993227,0.00006115549,0.0002694527,0.0001035473,0.0001742205,0.00006897824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.1045178,0.0005073934,0.8883978,0.00001167607,0.0003717598,0.00006717856,0.000299485,0.0001162843,0.0007594984,0.0001623785,0.00005029837,0.004738537],"study_design_scores_gemma":[0.001081143,0.1406593,0.8557068,0.00008468484,0.0001830555,0.0006783426,0.0002347676,0.0003135744,0.0006137355,0.0004124024,0.000001440384,0.00003084413],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972057,0.0001231741,0.001059396,0.001018158,0.0003843027,0.0001625483,0.000003312995,0.000005843787,0.00003760116],"genre_scores_gemma":[0.9995239,0.00006994003,0.0001023635,0.0001805587,0.0001110168,0.000002659997,4.786133e-7,0.00000489504,0.000004206381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1401519,"threshold_uncertainty_score":0.3844291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01462393924353017,"score_gpt":0.3230007589270344,"score_spread":0.3083768196835042,"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."}}