Akbulut S, Kucukakcali Z, Colak C. Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications. World J Gastroenterol 2025; 31(43): 112000 [DOI: 10.3748/wjg.v31.i43.112000]
Corresponding Author of This Article
Sami Akbulut, MD, Professor, Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Elazig Yolu 10. Km, Malatya 44280, Türkiye. akbulutsami@gmail.com
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Surgery
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Review
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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/
Nov 21, 2025 (publication date) through Nov 20, 2025
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World Journal of Gastroenterology
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1007-9327
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Akbulut S, Kucukakcali Z, Colak C. Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications. World J Gastroenterol 2025; 31(43): 112000 [DOI: 10.3748/wjg.v31.i43.112000]
World J Gastroenterol. Nov 21, 2025; 31(43): 112000 Published online Nov 21, 2025. doi: 10.3748/wjg.v31.i43.112000
Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications
Sami Akbulut, Zeynep Kucukakcali, Cemil Colak
Sami Akbulut, Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Malatya 44280, Türkiye
Sami Akbulut, Zeynep Kucukakcali, Cemil Colak, Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya 44280, Türkiye
Co-corresponding authors: Sami Akbulut and Cemil Colak.
Author contributions: Akbulut S, Kucukakcali Z, and Colak C contributed equally to the study design, data collection, analysis, manuscript writing, and revision; all authors have read and approved the final version of the manuscript.
Conflict-of-interest statement: The authors declare that they have 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: Sami Akbulut, MD, Professor, Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Elazig Yolu 10. Km, Malatya 44280, Türkiye. akbulutsami@gmail.com
Received: July 15, 2025 Revised: August 26, 2025 Accepted: October 14, 2025 Published online: November 21, 2025 Processing time: 128 Days and 10.3 Hours
Core Tip
Core Tip: This comprehensive review explores the emerging role of artificial intelligence (AI), including machine learning and deep learning techniques, in diagnosing acute appendicitis (AAp). Despite advancements in imaging and clinical scoring, diagnosing AAp remains challenging, particularly in atypical cases. AI models such as random forests, support vector machines, and convolutional neural networks have demonstrated promising results in enhancing diagnostic accuracy and decision-making. In addition to aiding in the differential diagnosis of AAp from other causes of acute abdominal pain, AI approaches have also been applied to distinguish between complicated and uncomplicated appendicitis, thereby supporting risk stratification and guiding management strategies. The review discusses current evidence, potential benefits, and limitations of integrating AI-based decision support into clinical practice. These insights may pave the way for more precise, timely, and individualized management of AAp.
