Ormeci MT, Kirkik D, Tulubas E. Bridging artificial intelligence and clinical decision-making in gastric cancer surgery. World J Gastrointest Surg 2026; 18(9): 119402 [DOI: 10.4240/wjgs.119402]
Corresponding Author of This Article
Duygu Kirkik, Associate Professor, Department of Immunology, Hamidiye Medicine Faculty, University of Health Sciences, Mekteb-i Tıbbiyye-i Sahane (Haydarpasa) Kulliyesi Selimiye Mah Tıbbiye Cad No. 38, Istanbul 34668, Türkiye. dygkirkik@gmail.com
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Gastroenterology & Hepatology
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review-article
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Ormeci MT, Kirkik D, Tulubas E. Bridging artificial intelligence and clinical decision-making in gastric cancer surgery. World J Gastrointest Surg 2026; 18(9): 119402 [DOI: 10.4240/wjgs.119402]
World J Gastrointest Surg. Sep 27, 2026; 18(9): 119402 Published online Sep 27, 2026. doi: 10.4240/wjgs.119402
Bridging artificial intelligence and clinical decision-making in gastric cancer surgery
Mehmet T Ormeci, Duygu Kirkik, Erkam Tulubas
Mehmet T Ormeci, Department of Radiology, University of Health Sciences, Istanbul 34668, Türkiye
Duygu Kirkik, Department of Immunology, Hamidiye Medicine Faculty, University of Health Sciences, Istanbul 34668, Türkiye
Erkam Tulubas, Uzm. Dr. Erkam Tulubas Muayenehanesi, Istanbul 34349, Türkiye
Co-first authors: Mehmet T Ormeci and Duygu Kirkik.
Author contributions: Ormeci MT and Kirkik D contributed equally to this article and are the co-first authors of this manuscript; Ormeci MT conceived the study and supervised the overall design; Ormeci MT, Kirkik D, and Tulubas E drafted the manuscript, and all authors have read and approved the final manuscript.
AI contribution statement: ChatGPT was used for grammatical editing and assistance in generating figures/images. The AI-assisted modifications did not alter the scientific content, interpretation, or conclusions of the manuscript.
Conflict-of-interest statement: The authors report no relevant conflicts of interest for this article.
Corresponding author: Duygu Kirkik, Associate Professor, Department of Immunology, Hamidiye Medicine Faculty, University of Health Sciences, Mekteb-i Tıbbiyye-i Sahane (Haydarpasa) Kulliyesi Selimiye Mah Tıbbiye Cad No. 38, Istanbul 34668, Türkiye. dygkirkik@gmail.com
Received: January 27, 2026 Revised: February 5, 2026 Accepted: June 1, 2026 Published online: September 27, 2026 Processing time: 232 Days and 1.1 Hours
Abstract
Postoperative survival among gastric cancer patients after potentially curative surgery can vary significantly. Therefore, a valid and individualized predictor of postoperative survival in gastric cancer patients who have undergone potentially curative resection is urgently needed. This article highlights the use of interpretable machine learning for individualized risk assessment in gastric cancer patients and provides initial insights into future directions, including multicenter studies incorporating deeper biological insights.
Core Tip: Artificial intelligence has the potential to transform gastric cancer surgery in multiple ways, including improving risk prediction for perioperative complications and enhancing surgical planning, treatment selection, and postoperative monitoring through the use of large-scale clinical data. Beyond achieving high predictive accuracy, artificial intelligence models must also be interpretable, transparent and clinically applicable. As these technologies continue to evolve, they are expected to become increasingly valuable tools in surgical practice. This article highlights how clinically transparent artificial intelligence models can support individualized risk stratification and inform postoperative decision-making in gastric cancer surgery.
