Machine Learning and Artificial Intelligence for Surgical Decision Making.

Journal: Surgical infections
Published Date:

Abstract

The use of machine learning (ML) and artificial intelligence (AI) in medical research continues to grow as the amount and availability of clinical data expands. These techniques allow complex interpretation of data and capture non-linear relations not immediately apparent by classic statistical techniques. This review of the ML/AI literature provides a brief overview for practicing surgeons and clinicians of the current and future roles these methods will have within surgical infection research. A conceptual overview of the techniques is provided along with concrete examples in the surgical infections literature. Further examples of ML/AI techniques in clinical decision support as well as therapy discovery with model-based deep reinforcement learning are illustrated. Artificial intelligence and ML are important and increasingly utilized techniques within the expanding body of surgical infection research. This article provides a minimal baseline literacy in ML/AI to be able to view such projects in an appropriately critical fashion.

Authors

  • Saskya Byerly
  • Lydia R Maurer
    Department of Surgery, Massachusetts General Hospital, Boston, MA.
  • Alejandro Mantero
    Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miami, Florida, USA.
  • Leon Naar
    Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
  • Gary An
    Department of Surgery, University of Vermont, USA.
  • Haytham M A Kaafarani
    Massachusetts General Hospital & Harvard Medical School, Boston, MA.