Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.

Journal: Langenbeck's archives of surgery
Published Date: Oct 29, 2021

Abstract

PURPOSE: An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various medical fields capitalized the application of machine learning (ML), and preliminary research suggests promising implications in surgeons' workflow. Hence, we evaluated ML's contemporary and possible future role in clinical decision-making (CDM) focusing on abdominal surgery.

Authors

  • Jonas Henn
    Department of General, Visceral, Thoracic and Vascular Surgery, University of Bonn, Bonn, Germany.
  • Andreas Buness
    Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany.
  • Matthias Schmid
    Department of Medical Biometry, Informatics and Epidemiology, Rheinische Friedrich-Wilhelms-Universität Bonn, Sigmund-Freud-Str. 25, 53127 Bonn, Germany. Electronic address: schmid@imbie.meb.uni-bonn.de.
  • Jörg C Kalff
    Department of General, Visceral, Thoracic and Vascular Surgery, University of Bonn, Bonn, Germany.
  • Hanno Matthaei
    Department of General, Visceral, Thoracic and Vascular Surgery, University of Bonn, Bonn, Germany. hanno.matthaei@ukbonn.de.

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