Arredondo Montero J. From the mathematical model to the patient: The scientific and human aspects of artificial intelligence in gastrointestinal surgery. World J Gastrointest Surg 2024; 16(6): 1517-1520 [PMID: PMC11230006 DOI: 10.4240/wjgs.v16.i6.1517]
Corresponding Author of This Article
Javier Arredondo Montero, MD, PhD, Pediatric Surgeon. Department of Pediatric Surgery, Complejo Asistencial Universitario de León, c/Altos de Nava s/n, Castilla y León, León 24008, Spain. javier.montero.arredondo@gmail.com
Research Domain of This Article
Surgery
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Surg. Jun 27, 2024; 16(6): 1517-1520 Published online Jun 27, 2024. doi: 10.4240/wjgs.v16.i6.1517
From the mathematical model to the patient: The scientific and human aspects of artificial intelligence in gastrointestinal surgery
Javier Arredondo Montero
Javier Arredondo Montero, Department of Pediatric Surgery, Complejo Asistencial Universitario de León, Castilla y León, León 24008, Spain
Author contributions: Arredondo Montero J manuscript conception and design; writing, review and editing; literature review.
Conflict-of-interest statement: The author has no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Javier Arredondo Montero, MD, PhD, Pediatric Surgeon. Department of Pediatric Surgery, Complejo Asistencial Universitario de León, c/Altos de Nava s/n, Castilla y León, León 24008, Spain. javier.montero.arredondo@gmail.com
Received: March 10, 2024 Revised: April 3, 2024 Accepted: April 22, 2024 Published online: June 27, 2024 Processing time: 111 Days and 11.6 Hours
Abstract
Recent medical literature shows that the application of artificial intelligence (AI) models in gastrointestinal pathology is an exponentially growing field, with promising models that show very high performances. Regarding inflammatory bowel disease (IBD), recent reviews demonstrate promising diagnostic and prognostic AI models. However, studies are generally at high risk of bias (especially in AI models that are image-based). The creation of specific AI models that improve diagnostic performance and allow the establishment of a general prognostic forecast in IBD is of great interest, as it may allow the stratification of patients into subgroups and, in turn, allow the creation of different diagnostic and therapeutic protocols for these patients. Regarding surgical models, predictive models of postoperative complications have shown great potential in large-scale studies. In this work, the authors present the development of a predictive algorithm for early post-surgical complications in Crohn's disease based on a Random Forest model with exceptional predictive ability for complications within the cohort. The present work, based on logical and reasoned, clinical, and applicable aspects, lays a solid foundation for future prospective work to further develop post-surgical prognostic tools for IBD. The next step is to develop in a prospective and multicenter way, a collaborative path to optimize this line of research and make it applicable to our patients.
Core Tip: Recent medical literature shows that the application of artificial intelligence models in gastrointestinal pathology is an exponentially growing field. In this work, the authors present the development of a predictive algorithm for early post-surgical complications in Crohn's disease based on a Random Forest model with exceptional predictive ability for complications within the cohort. The present work, based on logical and reasoned, clinical, and applicable aspects lays, a solid foundation for future prospective work to further develop post-surgical prognostic tools for inflammatory bowel disease.
