{"id":"W4415469381","doi":"10.1186/s12876-025-04011-w","title":"Artificial intelligence-assisted colonoscopy improves adenoma detection rates in routine colonoscopy practice: a single-center, retrospective, propensity score-matched study with concurrent controls","year":2025,"lang":"en","type":"article","venue":"BMC Gastroenterology","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ministry of Science and ICT, South Korea; National IT Industry Promotion Agency","keywords":"Colonoscopy; Hepatology; Adenoma; Propensity score matching; Endoscopy; MEDLINE","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":[0.002636022,0.0003082828,0.0004182192,0.0006780971,0.000468682,0.0008540623,0.0004363887,0.0004701564,0.001243653],"category_scores_gemma":[0.008083206,0.0003788975,0.0009137827,0.001161136,0.0004103321,0.0005062866,0.0003872813,0.000428115,0.0001832512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003893397,"about_ca_system_score_gemma":0.0004354659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001631136,"about_ca_topic_score_gemma":0.001496522,"domain_scores_codex":[0.9975546,0.0008697464,0.0003721992,0.0006607476,0.0003706021,0.0001722009],"domain_scores_gemma":[0.9932603,0.001184091,0.004157285,0.0007200122,0.0003462502,0.0003321382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008059841,0.0001132778,0.9965361,0.00002798416,0.0003788914,0.00005467173,0.0000548775,0.00007770894,0.000145274,0.00003512437,0.00009330651,0.00167679],"study_design_scores_gemma":[0.0001209312,0.000893454,0.9968066,0.00001477247,0.0003712911,0.0002978913,0.0001044094,0.0008528305,0.0001706204,0.00006369804,0.0002944893,0.000009003914],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984889,0.0004621042,0.000546373,0.00004829481,0.00001681983,0.00003306095,0.0001400523,0.000006000314,0.0002583752],"genre_scores_gemma":[0.99959,0.00005724952,0.0001605789,0.00002028999,0.00001394825,0.00001434546,0.0001053488,0.000001171517,0.00003704713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002636022,"threshold_uncertainty_score":0.01394075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04817909833684303,"score_gpt":0.3193498446807931,"score_spread":0.2711707463439501,"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."}}