Artificial Intelligence Methods and Artificial Intelligence-Enabled Metrics for Surgical Education: A Multidisciplinary Consensus.

Journal: Journal of the American College of Surgeons
Published Date: May 11, 2022

Abstract

BACKGROUND: Artificial intelligence (AI) methods and AI-enabled metrics hold tremendous potential to advance surgical education. Our objective was to generate consensus guidance on specific needs for AI methods and AI-enabled metrics for surgical education.

Authors

  • S Swaroop Vedula
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Ahmed Ghazi
    Department of Urology, University of Rochester Medical Center, Rochester, NY, USA.
  • Justin W Collins
  • Carla Pugh
    Department of Surgery, Stanford University School of Medicine, Stanford, California. Electronic address: cpugh@stanford.edu.
  • Dimitrios Stefanidis
    Carolinas Medical Center, Charlotte, North Carolina.
  • Ozanan Meireles
    - Harvard Medical School, Surgical Artificial Intelligence and Innovation Laboratory, Massachusetts General Hospital - Boston - MA - Estados Unidos.
  • Andrew J Hung
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, California. Electronic address: Andrew.Hung@med.usc.edu.
  • Steven Schwaitzberg
    Jacobs School of Medicine and Biomedical Sciences, The State University of New York, Buffalo, NY; Department of Surgery, The State University of New York, Buffalo, NY; Buffalo General Hospital, NY.
  • Jeffrey S Levy
    lnstitute for Surgical Excellence, Washington, District of Columbia.
  • Ajit K Sachdeva
    Division of Education, American College of Surgeons, Chicago, IL (Sachdeva).

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