The implementation of computer-aided detection in an initial endoscopy training improves the quality measures of trainees’ future colonoscopies: a retrospective cohort study

Endoscopists trained in colonoscopy using AI exceeded the aspirational targets of the quality guidelines when those trained conventionally achieved minimum quality measures.

Authors

Institutions

Publication Details

Journal
Surgical Endoscopy
Published
2025-06-30
DOI
https://doi.org/10.1007/s00464-025-11890-3
Citations
5
Primary Topic
Colorectal Cancer Screening and Detection
Type
article
Field-Weighted Citation Impact
5.23
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The implementation of computer-aided detection in an initial endoscopy training improves the quality measures of trainees’ future colonoscopies: a retrospective cohort study

Mirosław Szura, Tomasz Gach, Zofia Orzeszko, Kajetan Ochwat et al.
5 citations
Surgical Endoscopy
Colorectal Cancer Screening and Detection
5.23
article

The implementation of computer-aided detection in an initial endoscopy training improves the quality measures of trainees’ future colonoscopies: a retrospective cohort study

Mirosław Szura, Tomasz Gach, Zofia Orzeszko, Kajetan Ochwat, Piotr Major, Sandra Necka
article en
5 citations

Abstract

Endoscopists trained in colonoscopy using AI exceeded the aspirational targets of the quality guidelines when those trained conventionally achieved minimum quality measures.

Surgical EndoscopyVol. 39(8)
Jagiellonian University (PL)
Openalex Percentile: Top 4%
Colorectal Cancer Screening and Detection
5.23
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.