Deep Learning Applications in Surgery: Current Uses and Future Directions

Miranda X. Morris, Aashish Rajesh, Malke Asaad, Abbas Hassan, Rakan Saadoun, Charles E. Butler

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations

Abstract

Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data. Across various surgical specialties, DL is being applied to optimize both preoperative planning and intraoperative performance in new and innovative ways. Surgeons are now able to integrate deep learning tools into their practice to improve patient safety and outcomes. Through this review, we explore the applications of deep learning in surgery and related subspecialties with an aim to shed light on the practical utilization of this technology in the present and near future.

Original languageEnglish (US)
Pages (from-to)36-42
Number of pages7
JournalAmerican Surgeon
Volume89
Issue number1
DOIs
StatePublished - Jan 2023

Keywords

  • artificial intelligence
  • computer vision
  • deep learning
  • machine learning
  • neural networks
  • surgery
  • surgical innovation
  • surgical technology
  • Neural Networks, Computer
  • Humans
  • Machine Learning
  • Forecasting
  • Deep Learning
  • Medicine

ASJC Scopus subject areas

  • Surgery

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