Clinical Endoscopy

Systematic Review and Meta-analysis

To what extent can bowel preparation quality be considered “adequate”?: a systematic review and meta-analysis: Chen-Ya Kuo, David Karsenti, Salvador Machlab, Fu-Jen Lee, Yu-Tsung Chen, Te-Ling Ma, Chi-Yang Chang, Yu-Tse Chiu; Clin Endosc 2026;59(4):569-580. Published online July 30, 2026; DOI: https://doi.org/10.5946/ce.2026.022

Graphical Abstract Abstract PDF Supplementary Material: Background
/Aims: While low-quality bowel preparation is known to reduce the adenoma detection rate (ADR), the distinction between intermediate- and high-quality preparations remains unclear.
Methods
Electronic searches were conducted in PubMed and Embase through July 2024. Randomized controlled trials reporting ADR and the Boston bowel preparation scale (BBPS) were included, and outcomes were pooled according to bowel preparation quality using a random-effects model. The primary outcome was ADR in intermediate-quality (BBPS ≥6, and ≥2 in each segment) versus high-quality (BBPS≥8) bowel preparation. Secondary outcomes included advanced ADR.
Results
Fourteen trials were included (5,246 in the high-quality group and 1,728 in the other group). There was no significant difference in ADR (risk difference [RD], –0.02; p=0.13; I2=21%). Subgroup analyses showed no significant difference with computer-aided detection (CADe; RD, –0.03; p=0.70; I2=81%) or linked color imaging (RD, 0.04; p=0.31; I2=0%). The heterogeneity observed with CADe may reflect differences in colonoscopist experience or CADe systems. The results were similar for advanced ADR. Funnel plot and Egger’s test indicated minimal publication bias.
Conclusions
Intermediate-quality preparation allows adequate adenoma detection. When each BBPS subscore is ≥2, surveillance recommendations can generally be followed.

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Reviews

Translating artificial intelligence into clinical practice for gastrointestinal endoscopy: current applications and future perspectives: Hyeong Ho Jo, Jin Ho Choi, Joo Seong Kim, Seung-Joo Nam, Do Hoon Kim, Woo Hyun Paik, Jung Ho Bae, Seung Wook Hong, Chang Seok Bang, Da Hyun Jung, Seong Ji Choi, Hyunsoo Chung, The Research Group for Artificial Intelligence, Korean Society for Gastrointestinal Endoscopy; Received November 13, 2025 Accepted February 28, 2026 Published online July 30, 2026; DOI: https://doi.org/10.5946/ce.2025.419 [Epub ahead of print]

Abstract PDF: Artificial intelligence (AI) has emerged as a transformative tool in gastrointestinal (GI) endoscopy, addressing challenges in detection, diagnosis, and decision-making. In upper GI endoscopy, AI supports blind spot monitoring, Helicobacter pylori diagnosis, and the identification of premalignant and malignant lesions, with high accuracy and reduced miss rates. In lower GI endoscopy, computer-aided detection improves adenoma detection, whereas computer-aided diagnosis supports “resect-and-discard” and “diagnose-and-leave” strategies. However, real-world benefits remain modest, with concerns regarding overdetection and variable performance across lesion types and colon segments. In inflammatory bowel disease, AI standardizes endoscopic and histologic scoring, reduces interobserver variability, and accelerates capsule endoscopy interpretation, including high diagnostic accuracy for Crohn’s disease. Pancreatobiliary applications, including endoscopic ultrasound, endoscopic retrograde cholangiopancreatography, and cholangioscopy, demonstrate strong performance in differentiating pancreatic masses and biliary strictures and in predicting postprocedural complications. Despite expert-level performance across multiple domains, most studies remain single-center or retrospective, and explainability, workflow integration, medicolegal responsibility, and cost-effectiveness continue to limit adoption. Emerging solutions, including explainable AI and AI-generated common data model-compatible reports, may bridge these gaps. With rigorous multicenter validation and real-world implementation, AI can evolve from an experimental adjunct into a core component of routine endoscopic practice.

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The cutting-edge evolution of artificial intelligence-assisted capsule endoscopy: Dong Jun Oh, Yun Jeong Lim; Received November 12, 2025 Accepted January 20, 2026 Published online May 4, 2026; DOI: https://doi.org/10.5946/ce.2025.418 [Epub ahead of print]

Abstract PDF: Since its introduction in 2000, capsule endoscopy (CE) has transformed gastrointestinal (GI) diagnostics by enabling noninvasive visualization of the entire GI tract using a swallowable capsule. However, CE still has several limitations, including long reading times, inter-reader variability, and missed lesions due to poor image quality or incomplete examinations. Recent advances in artificial intelligence (AI) have significantly improved CE interpretation. Deep-learning models, particularly convolutional neural networks, can detect small-bowel lesions with accuracy comparable to that of expert endoscopists, while greatly reducing reading time. AI algorithms can also provide objective assessments of small-bowel cleanliness. Transformer-based models can further enhance video-level analysis by recognizing global patterns and sequential relationships among CE images. In addition, foundation models demonstrate high adaptability and robust performance across different CE systems and a wide range of lesion types. Future AI-assisted CE reading is expected to integrate real-time image analysis, autonomous capsule movement, and multimodal sensing technologies to create an intelligent diagnostic platform. Ultimately, AI is transforming CE into an efficient, reliable, and data-driven diagnostic tool suitable for diverse clinical settings. Furthermore, AI-assisted CE is extending its clinical utility beyond small-bowel lesion detection to the comprehensive evaluation of the stomach and colon.

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Original Articles

Impact of real-time artificial intelligence integration on detection of gastric lesions: an exploratory single-center before-and-after study using low-definition routine endoscopy: Hwijun Lee, Huynh Cong Bang, Seokho Cho, Jungmin Ha, Tran Thien Khiem, Le Viet Tung, La Vinh Phuc, Duong Trong Si, Tran The Du, Diem Thi-Ngoc Vo, Vo Nguyen Trung; Clin Endosc 2026;59(3):408-416. Published online April 22, 2026; DOI: https://doi.org/10.5946/ce.2025.272

Graphical Abstract Abstract PDF Supplementary Material

Background
/Aims: Early detection of gastric neoplasia, particularly subcentimeter lesions, using upper gastrointestinal (GI) endoscopy remains challenging. This study evaluated the impact of a real-time artificial intelligence (AI) detection system on the lesion detection rate (LDR) during routine upper GI endoscopy performed using a low-definition platform commonly used in resource-limited settings, with a focus on lesions ≤0.5 cm.
Methods
Diagnostic upper GI endoscopies performed between September 2024 and May 2025 were analyzed. LDRs were compared between the pre- and post-AI periods, including subgroup analyses by lesion size and type.
Results
A total of 2,329 patients were included (1,491 pre-AI, 838 post-AI). After AI implementation, overall LDR per person increased from 1.15±0.45 to 1.20±0.57 (p<0.05). Detection of lesions ≤0.5 cm increased from 18.0% to 19.8% (p<0.05), while detection of larger lesions remained unchanged. The biopsy rate decreased from 13.8% to 8.5% (p<0.05).
Conclusions
Real-time AI modestly improved the detection of diminutive gastric lesions while reducing unnecessary biopsies without compromising malignancy detection, thereby supporting its utility in routine endoscopy under resource-limited conditions.

Citations

Citations to this article as recorded by

Expanding role of artificial intelligence in gastric neoplasms: moving beyond resource limitations
Hannah Lee, Jun-Won Chung, Kyoung Oh Kim, Kwang An Kwon, Jung Ho Kim
Clinical Endoscopy.2026; 59(3): 400. CrossRef

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Linked color imaging versus artificial intelligence-assisted linked color imaging for neoplasia detection in the colorectum: a randomized trial in Brazil: Carlos Eduardo Oliveira dos Santos, Naohisa Yoshida, Asadur Jorge Tchekmedyian, Gabriel Malaman dos Santos, Luma Alves Costa, Ivan David Arciniegas Sanmartin, Júlio Pereira-Lima; Clin Endosc 2026;59(2):264-272. Published online March 27, 2026; DOI: https://doi.org/10.5946/ce.2025.276

Graphical Abstract Abstract PDF: Background
/Aims: Adenomas and sessile serrated lesions (SSLs) are neoplasms that play a role in colorectal cancer development. Improving adenoma detection rate (ADR) and SSL detection rates (SDR) allows for more effective prevention of colorectal cancer. Linked color imaging (LCI) and artificial intelligence (AI) have contributed to increased ADR and SDR. This study aimed to compare the neoplasia detection rates (NDR, adenomas, or SSLs) between LCI and AI-assisted LCI colonoscopy.
Methods
We conducted a prospective randomized trial to compare LCI with LCI+AI. We evaluated ADR, SDR, and NDR as the primary outcomes.
Results
A total of 779 polyps were detected in 622 patients (304 in the LCI group and 318 in the LCI+AI group); 555 were adenomas and 62 were SSLs for a total of 617 neoplastic lesions (79.2%) in 363 patients. Comparing the LCI and LCI+AI groups, the ADR, SDR, and NDR were 46.7% vs. 53.1% (p=0.13), 8.2% vs. 8.5% (p=0.90), and 52.3% vs. 56.6% (p=0.30), respectively. The mean number of adenomas per patient, advanced ADR, and withdrawal time did not significantly differ between the two groups.
Conclusions
Similar results were observed in both groups, and even with the good performance of AI-assisted LCI, LCI alone yielded high ADR, SDR, and NDR.

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Review

Recent technological advances in device-assisted enteroscopy: Mi Rae Lee, Jin Su Kim; Clin Endosc 2026;59(2):194-202. Published online March 3, 2026; DOI: https://doi.org/10.5946/ce.2025.136

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