Ardila CM, González-Arroyave D, Ramírez-Arbeláez J. Artificial intelligence as a predictive tool for gastric cancer: Bridging innovation, clinical translation, and ethical considerations. World J Gastrointest Oncol 2025; 17(5): 103275 [DOI: 10.4251/wjgo.v17.i5.103275]
Corresponding Author of This Article
Carlos M Ardila, PhD, Postdoctoral Fellow, Professor, Department of Basic Sciences, Biomedical Stomatology Research Group, Faculty of Dentistry, Universidad de Antioquia U de A, Calle 70 No. 52-21, Medellín 050010, Antioquia, Colombia. martin.ardila@udea.edu.co
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Letter to the Editor
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 Oncol. May 15, 2025; 17(5): 103275 Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.103275
Artificial intelligence as a predictive tool for gastric cancer: Bridging innovation, clinical translation, and ethical considerations
Carlos M Ardila, Daniel González-Arroyave, Jaime Ramírez-Arbeláez
Carlos M Ardila, Department of Basic Sciences, Biomedical Stomatology Research Group, Faculty of Dentistry, Universidad de Antioquia U de A, Medellín 050010, Antioquia, Colombia
Carlos M Ardila, Department of Periodontics, Saveetha Dental College, and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Saveetha, Saveetha 600077, India
Daniel González-Arroyave, Department of Surgery, Universidad Pontificia Bolivariana, Medellín 050010, Antioquia, Colombia
Jaime Ramírez-Arbeláez, Department of Transplantation, Hospital San Vicente Fundación, Rionegro 054047, Antioquia, Colombia
Author contributions: Ardila CM performed the conceptualization, data curation, data analysis, manuscript writing, and revision of the manuscript; González- Arroyave D and Ramírez-Arbeláez J performed the data curation, data analysis, and revision 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: Carlos M Ardila, PhD, Postdoctoral Fellow, Professor, Department of Basic Sciences, Biomedical Stomatology Research Group, Faculty of Dentistry, Universidad de Antioquia U de A, Calle 70 No. 52-21, Medellín 050010, Antioquia, Colombia. martin.ardila@udea.edu.co
Received: November 14, 2024 Revised: January 14, 2025 Accepted: February 7, 2025 Published online: May 15, 2025 Processing time: 182 Days and 22 Hours
Abstract
With gastric cancer ranking among the most prevalent and deadly malignancies worldwide, early detection and individualized prognosis remain essential for improving patient outcomes. This letter discusses recent advancements in artificial intelligence (AI)-driven predictive tools for gastric cancer, emphasizing a computed tomography-based radiomic model that achieved a predictive accuracy of area under the curve of 0.893 for treatment response in advanced cases undergoing neoadjuvant immunochemotherapy. AI offers promising avenues for predictive accuracy and personalized treatment planning in gastric oncology. Additionally, this letter highlights the comparison of these AI tools with traditional methodologies, demonstrating their potential to streamline clinical workflows and address existing gaps in risk stratification and early detection. Furthermore, this letter addresses the ethical considerations and the need for robust clinical-AI collaboration to achieve reliable, transparent, and unbiased outcomes. Strengthening cross-disciplinary efforts will be vital for the responsible and effective deployment of AI in this critical area of oncology.
Core Tip: Artificial intelligence (AI) is transforming gastric cancer management by enhancing early detection and individualized treatment planning through advanced predictive models. This letter highlights recent innovations in AI, such as a computed tomography-based radiomic model that predicts responses to neoadjuvant immunochemotherapy in advanced gastric cancer patients. By integrating AI-driven analysis with clinical factors, these tools offer substantial predictive accuracy, promising improved patient outcomes. However, to fully realize AI’s potential, ongoing collaboration is essential to address ethical, technical, and validation challenges, ensuring AI’s responsible integration into clinical oncology for effective, transparent, and patient-centered care.
