Artificial intelligence in colorectal surgery: an AI-powered systematic review
Open accessAbstract
Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications are and what can be reasonably expected. This AI-powered review assessed the role of AI in colorectal surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of PubMed, Embase, Scopus, Cochrane Library databases, and gray literature was conducted on all available articles on AI in colorectal surgery (from January 1 1997 to March 1 2021), aiming to define the perioperative applications of AI. Potentially eligible studies were identified using novel software powered by natural language processing (NLP) and machine learning (ML) technologies dedicated to systematic reviews. Out of 1238 articles identified, 115 were included in the final analysis. Available articles addressed the role of AI in several areas of interest. In the preoperative phase, AI can be used to define tailored treatment algorithms, support clinical decision-making, assess the risk of complications, and predict surgical outcomes and survival. Intraoperatively, AI-enhanced surgery and integration of AI in robotic platforms have been suggested. After surgery, AI can be implemented in the Enhanced Recovery after Surgery (ERAS) pathway. Additional areas of applications included the assessment of patient-reported outcomes, automated pathology assessment, and research. Available data on these aspects are limited, and AI in colorectal surgery is still in its infancy. However, the rapid evolution of technologies makes it likely that it will increasingly be incorporated into everyday practice.
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Emerging evidence suggests that AI-enabled applications can improve operational efficiency, clinical decision-making, patient triage, scheduling, and treatment planning when supported by appropriate governance, infrastructure, and workforce competencies.
“Similarly, emerging evidence suggests that AI-enabled applications can improve operational efficiency, clinical decision-making, patient triage, scheduling, and treatment planning when supported by appropriate governance, infrastructure, and workforce competencies”
From A Scoping Review of Implemented Innovations in Cancer Care: Implications for Pan-Canadian Scaling (2026) · cites this paper as [54]
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The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
Rayyan-a web and mobile app for systematic reviews.
Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016) · Systematic Reviews · doi:10.1186/s13643-016-0384-4 · Cited by 26,092 · see citing works
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A Scoping Review of Implemented Innovations in Cancer Care: Implications for Pan-Canadian Scaling
Sampalli, T., Tomblin Murphy, G., Peacock, S., Navaratnam, S., Domm, D., & MacKenzie, K. (2026) · Current Oncology · doi:10.3390/curroncol33070395
- Authors
- Spinelli, A., Carrano, F. M., Laino, M. E., Andreozzi, M., Koleth, G., Hassan, C., Repici, A., Chand, M., Savevski, V., & Pellino, G.
- Affiliations
- Humanitas University (IT)
- IRCCS Humanitas Research Hospital (IT)
- University of Naples Federico II (IT)
- Hospital Selayang (MY)
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences (GB)
- Universitat Autònoma de Barcelona (ES)
- Corresponding author
- Spinelli, A.
- ORCID
- Venue
- Techniques in Coloproctology
- Published
- 2023-02-21
- Volume
- 27
- Issue
- 8
- Pages
- 615-629
- ISSN
- 1123-6337 (ISSN-L)
- 1128-045X
- OpenAlex
- W4321436273
- Cited by
- 38
- References
- 2
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 17 · 2023: 2
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Open access
- green
- discovery.ucl.ac.uk
- Licence
- Locations
- Techniques in Coloproctology (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli" (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Oncology › Colorectal Cancer Surgical Treatments
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Keywords
- artificial intelligence, colorectal surgery, clinical decision support, surgical outcome prediction, robotic surgery, enhanced recovery after surgery, patient-reported outcomes, natural language processing, machine learning
- MeSH terms
- Artificial Intelligence, Colorectal Surgery, Humans, Robotics, Digestive System Surgical Procedures
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