Editorial
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Oct 24, 2024; 15(10): 1256-1263
Published online Oct 24, 2024. doi: 10.5306/wjco.v15.i10.1256
Precision at scale: Machine learning revolutionizing laparoscopic surgery
Carlos M Ardila, Daniel González-Arroyave
Carlos M Ardila, Biomedical Stomatology Research Group, Universidad de Antioquia U de A, Medellín 0057, Colombia
Daniel González-Arroyave, Department of Surgery, Pontificia Universidad Bolivariana, Medellín 0057, Colombia
Author contributions: Ardila CM performed the conceptualization, data curation, data analysis, manuscript writing, and revision of the manuscript; González-Arroyave D performed the data analysis, manuscript writing, and revision of the manuscript; all authors have read and approved the final version of the manuscript.
Conflict-of-interest statement: The authors declare having no conflicts 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, Doctor, MSc, PhD, Academic Editor, Academic Research, Associate Professor, Biomedical Stomatology Research Group, Universidad de Antioquia U de A, Calle 70 52-21, Medellín 0057, Colombia. martin.ardila@udea.edu.co
Received: March 24, 2024
Revised: August 10, 2024
Accepted: August 22, 2024
Published online: October 24, 2024
Processing time: 188 Days and 10.4 Hours
Core Tip

Core Tip: Integration of machine learning in laparoscopic surgery revolutionizes patient care, enhancing surgical precision and personalized treatment. Advanced imaging techniques, robotic systems, and virtual reality simulations powered by machine learning algorithms optimize procedural techniques and training methods. However, challenges such as data privacy and algorithm bias must be addressed for responsible deployment. Collaborations between clinicians, engineers, and data scientists drive innovation, shaping a future where minimally invasive surgery is safer, more effective, and accessible to all.