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Generative artificial intelligence in surgery.

Severin Rodler1, Conner Ganjavi2, Pieter De Backer3

  • 1USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA; Department of Urology, University Hospital of LMU Munich, Germany; Young Academic Working Group in Urologic Technology of the European Association of Urology, Arnhem, The Netherlands.

Surgery
|April 6, 2024
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Summary
This summary is machine-generated.

Generative artificial intelligence (GAI) offers transformative potential in surgery, aiding in preoperative planning, intraoperative decision-making, and postoperative care. This technology enhances data management and surgeon-AI interaction throughout the surgical journey.

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Area of Science:

  • Surgical Innovation
  • Artificial Intelligence Applications
  • Medical Informatics

Background:

  • Generative artificial intelligence (GAI) excels at processing and presenting complex information.
  • The integration of GAI into surgical workflows is an emerging field with significant potential.
  • Existing surgical practices can be enhanced through advanced AI capabilities.

Purpose of the Study:

  • To provide a comprehensive overview of current and future applications of GAI in surgery.
  • To explore GAI's role across the entire surgical continuum: preoperative, intraoperative, and postoperative phases.
  • To highlight GAI's potential in improving surgical planning, decision support, and patient management.

Main Methods:

  • This paper presents a perspective on GAI in surgery, synthesizing current knowledge and future trends.
  • It reviews GAI's capabilities in data extraction, information synthesis, and communication.
  • The analysis covers GAI's utility from preoperative planning to postoperative follow-up.

Main Results:

  • GAI can assist in preoperative planning by extracting patient data and providing procedural simulations.
  • Intraoperatively, GAI can document previously uncaptured data and aid decision-making.
  • Postoperatively, GAI supports patient discharge and follow-up, offering real-time feedback and data storage.

Conclusions:

  • GAI applications in surgery are evolving into specialized tools for specific tasks like data management and communication.
  • GAI holds significant potential to streamline surgical processes and enhance surgeon-AI collaboration.
  • The future of surgery may involve GAI playing a pivotal role in optimizing patient care and surgical outcomes.