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Updated: Jul 8, 2026

Emergency Undocking in Robotic Surgery: A Simulation Curriculum
06:48

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

Artificial Intelligence in Surgical Training: A Comprehensive Update on Simulation, Assessment, and Emerging

Rushabh Shah1, Yash Verma1, Daniyal Ashraf2

  • 1Department of Plastic and Reconstructive Surgery, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, CB2 0QQ Cambridge, UK.

British Journal of Hospital Medicine (London, England : 2005)
|July 7, 2026
PubMed
Summary
This summary is machine-generated.

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Artificial intelligence (AI) is transforming surgical education through simulation, assessment, and recruitment. Emerging technologies like digital twins and generative AI will further shape future surgical training.

Area of Science:

  • Surgical Education
  • Medical Simulation
  • Artificial Intelligence in Medicine

Background:

  • Artificial intelligence (AI) is increasingly influencing various aspects of medical education.
  • Surgical training traditionally relies on apprenticeship models, facing challenges in standardization and accessibility.

Purpose of the Study:

  • To summarize recent advancements in AI for surgical education, covering simulation, assessment, and recruitment.
  • To explore the challenges and governance issues associated with implementing AI in surgical training.
  • To consider future trends and emerging AI technologies impacting surgical education.

Main Methods:

  • Review of recent developments in AI-driven surgical simulation platforms.
  • Analysis of AI applications in surgical skills assessment and trainee recruitment.
Keywords:
artificial intelligencemachine learningsimulationskill assessmentsurgical training

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Emergency Undocking in Robotic Surgery: A Simulation Curriculum
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Published on: May 20, 2018

  • Examination of literature on barriers to AI implementation in medical education.
  • Discussion of emerging technologies like digital twins and generative AI in the context of surgical training.

    • Main Results:

    • AI is enhancing surgical simulation fidelity and providing objective assessment metrics.
    • AI tools are being explored for more efficient and equitable surgical trainee recruitment.
    • Significant barriers to AI implementation include data privacy, ethical concerns, and the need for robust governance frameworks.
    • Emerging technologies promise more personalized and adaptive surgical training experiences.

    Conclusions:

    • Artificial intelligence offers transformative potential for surgical education, improving training efficiency and outcomes.
    • Addressing implementation barriers and establishing clear governance are crucial for successful AI integration.
    • Future surgical training will likely incorporate advanced AI tools, including digital twins and generative AI, for enhanced learning and decision support.