Artificial Intelligence in Surgical Research: Accomplishments and Future Directions.

Journal: American journal of surgery
PMID: 37981516

Abstract

MINI-ABSTRACT: The study introduces various methods of performing conventional ML and their implementation in surgical areas, and the need to move beyond these traditional approaches given the advent of big data.

Authors

  • Michael P Rogers
    OnetoMAP Data Analytics and Machine Learning, Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Haroon M Janjua
    Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Steven Walczak
    School of Information and Florida Center for Cybersecurity, University of South Florida, 4202 E. Fowler Ave., CIS 1040, Tampa, FL, 33620, USA. swalczak@usf.edu.
  • Marshall Baker
    Department of Surgery, Loyola University Medical Center, Maywood, IL; Edward Hines, Jr Veterans Administration Hospital, Hines, IL.
  • Meagan Read
    Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Konrad Cios
    Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Vic Velanovich
    Division of General Surgery, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
  • Ricardo Pietrobon
    SporeData Inc., Durham, NC (Pietrobon).
  • 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.

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