{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002167962,0.0001959142,0.0002606053,0.001063787,0.0003270136,0.0006664917,0.0003693302,0.000424046,0.001055439],"category_scores_gemma":[0.009902125,0.0002606997,0.0007267863,0.001348778,0.0005577301,0.0005809632,0.0005958973,0.0004754136,0.0001779339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000528998,"about_ca_system_score_gemma":0.0005105296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002313762,"about_ca_topic_score_gemma":0.002344307,"domain_scores_codex":[0.9976723,0.0009309184,0.0004120057,0.00026391,0.0005351382,0.0001857098],"domain_scores_gemma":[0.9843779,0.004893272,0.007741173,0.0008613813,0.001239811,0.0008864466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006109208,0.0000228074,0.9986519,0.00001619143,0.00002832361,0.00007841416,0.00004730835,0.00001487018,0.00003825386,0.000005716501,0.0000207153,0.001014459],"study_design_scores_gemma":[0.00000472184,0.0003654644,0.9979168,0.00002217266,0.00005680299,0.0009094499,0.0002973126,0.00009214848,0.00007865058,0.000007250404,0.0002442259,0.000005122058],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998421,0.000827652,0.0001097177,0.00005041255,0.000003918456,0.00001728749,0.0001048114,0.000002683409,0.0004626596],"genre_scores_gemma":[0.9994887,0.0002510702,0.0000855934,0.00003144289,0.000008158431,0.000006995114,0.00008107679,0.000001337915,0.00004558826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002313762,"threshold_uncertainty_score":0.01146537,"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."}}