From the mathematical model to the patient: The scientific and human aspects of artificial intelligence in gastrointestinal surgery

doi:10.4240/wjgs.v16.i6.1517

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Jun 27, 2024 (publication date) through Jun 29, 2024

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World Journal of Gastrointestinal Surgery

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1948-9366

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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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

Core Tip

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.