The future surgical training paradigm: Virtual reality and machine learning in surgical education.

Journal: Surgery
Published Date:

Abstract

Surgical training has undergone substantial change in the last few decades. As technology and patient complexity continues to increase, demands for novel approaches to ensure competency have arisen. Virtual reality systems augmented with machine learning represents one such approach. The ability to offer on-demand training, integrate checklists, and provide personalized, surgeon-specific feedback is paving the way to a new era of surgical training. Machine learning algorithms that improve over time as they acquire more data will continue to refine the education they provide. Further, fully immersive simulated environments coupled with machine learning analytics provide real-world training opportunities in a safe atmosphere away from the potential to harm patients. Careful implementation of these technologies has the potential to increase access and improve quality of surgical training and patient care and are poised to change the landscape of current surgical training. Herein, we describe the current state of virtual reality coupled with machine learning for surgical training, future directions, and existing limitations of this technology.

Authors

  • Michael P Rogers
    OnetoMAP Data Analytics and Machine Learning, Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Anthony J DeSantis
    OnetoMAP Data Analytics and Machine Learning, Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Haroon Janjua
    Department of Surgery, University of South Florida, Tampa, FL; OnetoMap Analytics, University of South Florida, Tampa, FL.
  • Tara M Barry
    Department of Surgery, University of South Florida, Tampa, FL; OnetoMap Analytics, University of South Florida, Tampa, FL.
  • Paul C Kuo
    Loyola University Medical Center, Department of Surgery, 2160 S. 1st Avenue, Maywood, IL 60153, USA; One:MAP Section of Surgical Analytics, Department of Surgery, Loyola University Chicago, 2160 S. 1st Avenue, Maywood, IL 60153, USA. Electronic address: paul.kuo@luhs.org.