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Novel applications of deep learning in surgical training

  • Shidin Balakrishnan
  • , Sarada Prasad Dakua
  • , Walid El Ansari
  • , Omar Aboumarzouk
  • , Abdulla Al Ansari
  • Hamad Medical Corporation
  • Qatar University
  • Weill Cornell Medicine-Qatar

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

Abstract

This chapter explores the novel applications of artificial intelligence (AI), specifically deep learning (DL) in surgical training. It aims to clarify the concepts associated with DL and address implementation approaches that integrate DL techniques with simulation and virtual reality, intelligent tutoring systems, augmented reality, robotic-assisted surgery, and data-driven personalized training. The chapter examines the innovative role of DL in creating realistic surgical simulations, enhancing adaptive learning experiences, and facilitating real-time feedback. The potential of DL to revolutionize surgical training, improve skill acquisition speed, and elevate patient outcomes is emphasized. Challenges, such as data availability, model transparency, and ethical considerations, are discussed. The chapter underscores the importance of interdisciplinary collaboration between surgeons, educators, and AI experts for the successful integration and future development of DL in surgical training.

Original languageEnglish
Title of host publicationArtificial Intelligence, Big Data, Blockchain and 5G for the Digital Transformation of the Healthcare Industry
Subtitle of host publicationa Movement Toward more Resilient and Inclusive Societies
PublisherElsevier
Pages301-320
Number of pages20
ISBN (Electronic)9780443215988
ISBN (Print)9780443215995
DOIs
StatePublished - 1 Jan 2023
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 4 - Quality Education
    SDG 4 Quality Education

Keywords

  • Artificial intelligence
  • Deep learning
  • Surgery
  • Surgical training

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