Annals of Coloproctology

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Review

Anorectal benign disease

Artificial intelligence in anal fistula: mapping evidence to IDEAL stages: Vipul D Yagnik, Prema Ram Choudhary, Pankaj Garg; Ann Coloproctol. 2026;42(3):264-272. Published online June 25, 2026; DOI: https://doi.org/10.3393/ac.2025.01494.0213

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Abstract PDF: Artificial intelligence (AI) is increasingly applied in colorectal and anorectal surgery, particularly for complex conditions such as anal fistula (AF). Conventional diagnostic and therapeutic approaches remain limited by intricate anatomy, high recurrence risk, and the need to preserve continence. This narrative review, evaluates the role of AI in AF and related anorectal disorders, with evidence mapped to the IDEAL (idea, development, exploration, assessment, and long-term follow-up) framework (stages 1–4, with stage 2 subdivided into 2a and 2b). Relevant literature was identified through targeted searches of PubMed, Embase, and Scopus. Studies investigating AI applications in AF or related anorectal conditions, including imaging, surgical planning, predictive modeling, and functional assessment, were included. Evidence was categorized according to IDEAL stages, ranging from proof-of-concept to long-term quality assurance. AI demonstrates potential across 4 key domains: (1) preoperative imaging (stages 1–2b); (2) intraoperative planning and assistance (stages 1–2a); (3) predictive modeling (stages 2a–2b); and (4) broader anorectal applications, including anorectal manometry and the EndoFLIP (endoluminal functional lumen imaging probe) procedure (stages 1–2b). Feasibility studies report high diagnostic performance, particularly for magnetic resonance imaging and computed tomography–based deep learning models; however, these findings are constrained by small sample sizes, limited external validation, and challenges related to workflow integration. Overall, AI has the potential to enhance diagnostic accuracy, surgical planning, and functional assessment in AF and related disorders. However, most studies remain within early IDEAL stages (1–2b), highlighting the need for multicenter validation, cost-effectiveness analyses, and robust ethical frameworks before widespread clinical implementation.

Original Article

Colorectal cancer

How appropriately can generative artificial intelligence platforms, including GPT-4, Gemini, Bing, and Wrtn, answer questions about colon cancer in the Korean language?: Sun Huh; Ann Coloproctol. 2025;41(3):190-197. Published online June 25, 2025; DOI: https://doi.org/10.3393/ac.2024.00122.0017

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Abstract PDF Supplementary Material

Purpose
This study aims to assess the performance of 4 generative artificial intelligence (AI) platforms—Gemini (formerly Bard), Bing, GPT-4, and Wrtn—in answering questions about colon cancer in the Korean language. Two main research questions guided this study. First, which AI platform provides the most accurate answers? Second, can these AI-generated answers be reliably used to educate patients and their families about colon cancer?
Methods
Ten questions selected by the author were posed to the 4 generative AI platforms on February 22, 2024. Two colorectal surgeons in Korea, each with over 20 years of clinical experience, independently evaluated the answers provided by these generative AI platforms.
Results
The generative AI platforms scored an average of 5.5 out of 10 points. Wrtn achieved the highest score at 6 points, followed by GPT-4 and Gemini, each with 5.5, and Bing, scoring 5 points. The weighted κ for inter-rater reliability was 0.597 (P<0.001). The generative AI platforms performed well in explaining the occult blood test for cancer screening, keyhole surgery, and dietary recommendations for cancer prevention. However, they demonstrated significant limitations in answering more complex topics, such as estimating survival rates following surgery, choosing targeted therapy after surgery, and accurately reporting the mortality rate due to colon cancer in Korea.
Conclusion
The findings suggest that using these generative AI platforms as educational resources for patients and their families regarding colon cancer is premature. Further training on colorectal diseases is required before these AI platforms can be considered reliable information sources for the general public in Korea.

Citations

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Expert Review on the Quality of Responses to the Questions of Multiple Myeloma Patients: A Validation Study of the Medical Artificial Intelligence System “Myelobot”
Aleksander Sergeevich Luchinin, O. E. Ochirova, V. G. Potapenko, V. V. Ryabchikova
Clinical Oncohematology.2026; 19(1): 81. CrossRef
Agentic artificial intelligence is the future of cancer detection and diagnosis
Sayedur Rahman, Md. Tanzib Hosain, Nafiz Fahad, Md. Kishor Morol, Md. Jakir Hossen
Array.2026; 29: 100676. CrossRef
Artificial intelligence in gastroenterology clinical practice: Scoping review of large language model applications
Yigit Yazarkan, Gamze Sonmez, Cem Simsek
International Journal of Medical Informatics.2026; 214: 106413. CrossRef
Role of Medical Editors in the Age of Generative Artificial Intelligence
Sun Huh
Healthcare Informatics Research.2025; 31(4): 317. CrossRef
Temporal evolution of large language models (LLMs) in oncology
Zilin Qiu, Aimin Jiang, Chang Qi, Wenyi Gan, Lingxuan Zhu, Weiming Mou, Dongqiang Zeng, Mingjia Xiao, Guangdi Chu, Shengkun Peng, Hank Z. H. Wong, Lin Zhang, Hengguo Zhang, Xinpei Deng, Quan Cheng, Bufu Tang, Yaxuan Wang, Jian Zhang, Anqi Lin, Peng Luo
Journal of Translational Medicine.2025;[Epub] CrossRef

Reviews

Minimally invasive surgery

Robotic colorectal surgery training: Portsmouth perspective: Guglielmo Niccolò Piozzi, Sentilnathan Subramaniam, Diana Ronconi Di Giuseppe, Rauand Duhoky, Jim S. Khan; Ann Coloproctol. 2024;40(4):350-362. Published online August 30, 2024; DOI: https://doi.org/10.3393/ac.2024.00444.0063

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9 Web of Science
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Abstract PDF

This study aims to discuss the principles and pillars of robotic colorectal surgery training and share the training pathway at Portsmouth Hospitals University NHS Trust. A narrative review is presented to discuss all the relevant and critical steps in robotic surgical training. Robotic training requires a stepwise approach, including theoretical knowledge, case observation, simulation, dry lab, wet lab, tutored programs, proctoring (in person or telementoring), procedure-specific training, and follow-up. Portsmouth Colorectal has an established robotic training model with a safe stepwise approach that has been demonstrated through perioperative and oncological results. Robotic surgery training should enable a trainee to use the robotic platform safely and effectively, minimize errors, and enhance performance with improved outcomes. Portsmouth Colorectal has provided such a stepwise training program since 2015 and continues to promote and augment safe robotic training in its field. Safe and efficient training programs are essential to upholding the optimal standard of care.

Citations

Citations to this article as recorded by

Technical proficiency assessment of robotic intracorporeal single-stapling colorectal anastomosis using video-based RA-CUSUM
Shih-Feng Huang, Yung-Lin Tan, Chao-Wen Hsu, Hsin-Ping Tseng, Danilo Miskovic, Chih-Chien Wu
International Journal of Colorectal Disease.2026;[Epub] CrossRef
Learning curve for Da Vinci Single-Port robotic colorectal cancer surgery: impact of prior robotic experience
Soo Young Lee, Chang Hyun Kim, Jaram Lee, Hyeung-min Park, Hyeong Rok Kim
Surgical Endoscopy.2026; 40(6): 4742. CrossRef
Adjacent-room dual-console Remote Surgical Training (ReST) with takeover capability on the da Vinci Xi: a porcine-model feasibility study
Lorenzo Spirito, Carmine Sciorio, Vittorio Imperatore, Antonio Di Girolamo, Giuseppe Romeo, Riccardo Giannella, Antonio Ruffo, Fabio Esposito, Lorenzo Romano, Paola Coppola, Luigi Napolitano, Antonio D’Ambrosio, Roberta Siciliano, Guido De Sena
Journal of Robotic Surgery.2026;[Epub] CrossRef
Pre-Procedural Robotic Surgery Curricula: A Systematic Review and Thematic Meta-Synthesis
Michael Devine, Carolyn Cullinane, Helen Mohan, Dara O. Kavanagh, Dara O’Keeffe, Barry B. McGuire, Christina A. Fleming
Annals of Surgery Open.2026; 7(2): e682. CrossRef
Robotic-assisted colorectal surgery in colorectal cancer management: a narrative review of clinical efficacy and multidisciplinary integration
Engeng Chen, Li Chen, Wei Zhang
Frontiers in Oncology.2025;[Epub] CrossRef
Entwicklung und Implementation eines strukturierten Ausbildungsprogramms in der robotischen Chirurgie
Sarah Englert, Natascha Tschukewitsch, Alexa Wölfl, Christoph Justinger
Die Chirurgie.2025; 96(9): 765. CrossRef
The evolution of training in robotic colorectal surgery
R. Smyth, N. Francis, S. Vasudevan
Journal of Robotic Surgery.2025;[Epub] CrossRef
Evaluating the Toumai MT‑1000 for urologic surgery: a systematic review and single-arm meta-analysis with remote and on-site experiences
Chi Zhang, Jinwan Wang
Journal of Robotic Surgery.2025;[Epub] CrossRef
A systematic review of comprehensive Robotic-assisted surgical (RAS) curricula
Anna K. Kieslich, Ruari Jardine, Hussain Ibrahim, Areeg Calvert, Kenneth G. Walker, Kim A. Walker, Angus J. M. Watson
Journal of Robotic Surgery.2025;[Epub] CrossRef
From the Editor: Uniting expertise, a new era of global collaboration in coloproctology
In Ja Park
Annals of Coloproctology.2024; 40(4): 285. CrossRef

Colorectal cancer

Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review: Minsung Kim, Taeyong Park, Bo Young Oh, Min Jeong Kim, Bum-Joo Cho, Il Tae Son; Ann Coloproctol. 2024;40(1):13-26. Published online February 28, 2024; DOI: https://doi.org/10.3393/ac.2023.00892.0127

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11 Web of Science
11 Citations

Abstract PDF

Purpose
The integration of artificial intelligence (AI) and magnetic resonance imaging in rectal cancer has the potential to enhance diagnostic accuracy by identifying subtle patterns and aiding tumor delineation and lymph node assessment. According to our systematic review focusing on convolutional neural networks, AI-driven tumor staging and the prediction of treatment response facilitate tailored treatment strategies for patients with rectal cancer.
Methods
This paper summarizes the current landscape of AI in the imaging field of rectal cancer, emphasizing the performance reporting design based on the quality of the dataset, model performance, and external validation.
Results
AI-driven tumor segmentation has demonstrated promising results using various convolutional neural network models. AI-based predictions of staging and treatment response have exhibited potential as auxiliary tools for personalized treatment strategies. Some studies have indicated superior performance than conventional models in predicting microsatellite instability and KRAS status, offering noninvasive and cost-effective alternatives for identifying genetic mutations.
Conclusion
Image-based AI studies for rectal cancer have shown acceptable diagnostic performance but face several challenges, including limited dataset sizes with standardized data, the need for multicenter studies, and the absence of oncologic relevance and external validation for clinical implantation. Overcoming these pitfalls and hurdles is essential for the feasible integration of AI models in clinical settings for rectal cancer, warranting further research.

Citations

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Artificial intelligence in CT for predicting lymph node metastasis in rectal cancer patients: a meta-analysis
D. Hou, H. Her, W. Han, X. Ge
Clinical Radiology.2026; 92: 107001. CrossRef
MRI to guide clinical management of rectal cancer: updated consensus recommendations from the European Society of Gastrointestinal and Abdominal Radiology (ESGAR)—PART I primary staging
Juan-Ramón Ayuso, Svetlana Balyaniskova, Regina G. H. Beets-Tan, Ivana Blazic, Lennart Blomqvist, Damiano Caruso, Filippo Crimì, Luís Curvo-Semedo, Raphaëla C. Dresen, Marc J. Gollub, Vicky Goh, Kirsten Gormly, Sofia Gourtsoyianni, Bengi Gurses, Christine
European Radiology.2026; 36(6): 4592. CrossRef
Radiomics’ Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review
Huda Mohammed, Hadeel Mohamed, Momoh Fofana, Samreen Jawaid, Mohamed Hersi, Omneya Alwani, Roshith Nair, Jayesh Sagar
Cancers.2026; 18(9): 1440. CrossRef
From regression to machine learning: improving prediction of rectal cancer recurrence
Thanat Tantinam, Ekkarin Supatrakul, Pawit Sutharat, Suwan Sanmee, Kullawat Bhatanaprabhabhan, Boonchai Ngamsirimas, Nataphon Santrakul, Rangsima Thiengthiantham, Punnawat Chandrachamnong, Suradet Buakhrun, Sarawut Ramjan
Annals of Coloproctology.2026; 42(3): 293. CrossRef
Use of artificial intelligence in analysis of endoscopic images to detect residual disease or regrowth in rectal patients with complete clinical response to neoadjuvant chemoradiotherapy
M. A. Javed, M. Mascarenhas, F. Mendes, E. Carvalho, R. Rajan, A. Santos, Z. Khan, I. Blake, S. Ahmed
Techniques in Coloproctology.2026;[Epub] CrossRef
Enhancing the role of MRI in rectal cancer: advances from staging to prognosis prediction
Xiaoling Gong, Zheng Ye, Yu Shen, Bin Song
European Radiology.2025; 35(9): 5714. CrossRef
Non-operative management of locally advanced rectal cancer with an emphasis on outcomes and quality of life: a narrative review
In Ja Park
Ewha Medical Journal.2025; 48(3): e40. CrossRef
L’intelligence artificielle pourrait-elle aider le chirurgien digestif dans la prise en charge du cancer du rectum ?
Arnaud Alves, Karem Slim
Journal de Chirurgie Viscérale.2024; 161(4): 253. CrossRef
Can artificial intelligence help a digestive surgeon in management of rectal cancer?
Arnaud Alves, Karem Slim
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Artificial intelligence for the colorectal surgeon in 2024 – A narrative review of Prevalence, Policies, and (needed) Protections
Kurt S. Schultz, Michelle L. Hughes, Warqaa M. Akram, Anne K. Mongiu
Seminars in Colon and Rectal Surgery.2024; 35(3): 101037. CrossRef
Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications
Joana Mota, Maria João Almeida, Miguel Martins, Francisco Mendes, Pedro Cardoso, João Afonso, Tiago Ribeiro, João Ferreira, Filipa Fonseca, Manuel Limbert, Susana Lopes, Guilherme Macedo, Fernando Castro Poças, Miguel Mascarenhas
Journal of Clinical Medicine.2024; 13(19): 5842. CrossRef

AI colonoscopy

The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists’ perceptions thereof: Sarah Tham, Frederick H. Koh, Jasmine Ladlad, Koy-Min Chue, SKH Endoscopy Centre, Cui-Li Lin, Eng-Kiong Teo, Fung-Joon Foo; Ann Coloproctol. 2023;39(5):385-394. Published online March 10, 2023; DOI: https://doi.org/10.3393/ac.2022.00878.0125

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4 Web of Science
4 Citations

Abstract PDF

The development of deep learning systems in artificial intelligence (AI) has enabled advances in endoscopy, and AI-aided colonoscopy has recently been ushered into clinical practice as a clinical decision-support tool. This has enabled real-time AI-aided detection of polyps with a higher sensitivity than the average endoscopist, and evidence to support its use has been promising thus far. This review article provides a summary of currently published data relating to AI-aided colonoscopy, discusses current clinical applications, and introduces ongoing research directions. We also explore endoscopists’ perceptions and attitudes toward the use of this technology, and discuss factors influencing its uptake in clinical practice.

Citations

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Translational Validation of a Novel Multi-Locus ctDNA Methylation Assay for Early Detection and Stratification of Colorectal Cancer: An Exploratory Prospective, Case-Control Study
Hayoung Lee, Jae Cheol Kang, In Ja Park, Gwang-un Kim, Hwi Hyun, Na Young Min, Sungwon Jeon, Byoung-Chul Kim
International Journal of Molecular Sciences.2026; 27(13): 5738. CrossRef
New Concept of Colonoscopy Assisted by a Microwave-Based Accessory Device: First Clinical Experience
Oswaldo Ortiz, Oriol Sendino, Silvia Rivadulla, Alejandra Garrido, Luz María Neira, Josep Sanahuja, Pilar Sesé, Marta Guardiola, Glòria Fernández-Esparrach
Cancers.2025; 17(7): 1073. CrossRef
Deep learning model for gastrointestinal polyp segmentation
Zitong Wang, Zeyi Wang, Pengyu Sun
PeerJ Computer Science.2025; 11: e2924. CrossRef
Does AI have utility in medical student surgical education? A comparative analysis of chatbots in answering standardized surgical multiple-choice questions
Natalia DaFonte, Angelo Cadiente, Catherine Implicito, Natasha Becker, Burton Surick
Global Surgical Education - Journal of the Association for Surgical Education.2025;[Epub] CrossRef

The Future Medical Science and Colorectal Surgeons: Young Jin Kim; Ann Coloproctol. 2017;33(6):207-209. Published online December 31, 2017; DOI: https://doi.org/10.3393/ac.2017.33.6.207

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64 Download
6 Web of Science
7 Citations

Abstract PDF

Future medical technology breakthroughs will build from the incredible progress made in computers, biotechnology, and nanotechnology and from the information learned from the human genome. With such technology and information, computer-aided diagnoses, organ replacement, gene therapy, personalized drugs, and even age reversal will become possible. True 3-dimensional system technology will enable surgeons to envision key clinical features and will help them in planning complex surgery. Surgeons will enter surgical instructions in a virtual space from a remote medical center, order a medical robot to perform the operation, and review the operation in real time on a monitor. Surgeons will be better than artificial intelligence or automated robots when surgeons (or we) love patients and ask questions for a better future. The purpose of this paper is looking at the future medical science and the changes of colorectal surgeons.

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Application and Prospect of a Mobile Hospital in Disaster Response
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