Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications.

Journal: The American surgeon
Published Date:

Abstract

Surgical complications pose significant challenges for surgeons, patients, and health care systems as they may result in patient distress, suboptimal outcomes, and higher health care costs. Artificial intelligence (AI)-driven models have revolutionized the field of surgery by accurately identifying patients at high risk of developing surgical complications and by overcoming several limitations associated with traditional statistics-based risk calculators. This article aims to provide an overview of AI in predicting surgical complications using common machine learning and deep learning algorithms and illustrates how this can be utilized to risk stratify patients preoperatively. This can form the basis for discussions on informed consent based on individualized patient factors in the future.

Authors

  • Abbas M Hassan
    Department of Plastic & Reconstructive Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Aashish Rajesh
    Department of Surgery, University of Texas Health Science Center, San Antonio, TX, USA.
  • Malke Asaad
    From the Division of Plastic Surgery, Department of Surgery, Mayo Clinic; the Division of Plastic Surgery, Department of Surgery, Sidra Medicine; and the Department of Surgery, Weill-Cornell Medical College-Qatar.
  • Jonas A Nelson
    Department of Plastic & Reconstructive Surgery, 5803Memorial Sloan Kettering Cancer Center, New York, NY.
  • J Henk Coert
    Department of Plastic and Reconstructive Surgery, 8124University Medical Center Utrecht, Utrecht, Netherlands.
  • Babak J Mehrara
    Department of Plastic & Reconstructive Surgery, 5803Memorial Sloan Kettering Cancer Center, New York, NY.
  • Charles E Butler
    Department of Plastic & Reconstructive Surgery, the University of Texas MD Anderson Cancer Center, Houston, TX, USA.