Ardila CM, González-Arroyave D. Precision at scale: Machine learning revolutionizing laparoscopic surgery. World J Clin Oncol 2024; 15(10): 1256-1263 [DOI: 10.5306/wjco.v15.i10.1256]
Corresponding Author of This Article
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
Research Domain of This Article
Computer Science, Artificial Intelligence
Article-Type of This Article
Editorial
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 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
Abstract
In their recent study published in the World Journal of Clinical Cases, the article found that minimally invasive laparoscopic surgery under general anesthesia demonstrates superior efficacy and safety compared to traditional open surgery for early ovarian cancer patients. This editorial discusses the integration of machine learning in laparoscopic surgery, emphasizing its transformative potential in improving patient outcomes and surgical precision. Machine learning algorithms analyze extensive datasets to optimize procedural techniques, enhance decision-making, and personalize treatment plans. Advanced imaging modalities like augmented reality and real-time tissue classification, alongside robotic surgical systems and virtual reality simulations driven by machine learning, enhance imaging and training techniques, offering surgeons clearer visualization and precise tissue manipulation. Despite promising advancements, challenges such as data privacy, algorithm bias, and regulatory hurdles need addressing for the responsible deployment of machine learning technologies. Interdisciplinary collaborations and ongoing technological innovations promise further enhancement in laparoscopic surgery, fostering a future where personalized medicine and precision surgery redefine patient care.
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.
