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Predicting Surgical Outcomes and Optimizing Patients for Surgery Using AI.

Neal Bhutiani1,2,3, Abhineet Uppal4

  • 1Divisions of Surgical Oncology, Department of Surgery, University of Louisville, Louisville, Kentucky, United States.

Clinics in Colon and Rectal Surgery
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Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) enhance physician risk assessment for surgical patients. These technologies optimize perioperative care and improve early detection of postoperative complications.

Keywords:
artificial intelligencemachine learningperioperative caresurgical outcomes

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Area of Science:

  • Perioperative medicine
  • Medical informatics
  • Surgical outcomes

Background:

  • Electronic health data and AI/ML algorithms offer new predictive capabilities.
  • Current perioperative tools can be refined or reimagined using AI/ML.
  • Physicians' risk assessment and predictive abilities can be augmented.

Purpose of the Study:

  • Review AI and ML construction and implementation in surgery.
  • Assess the current use of predictive models in perioperative surgical patients.
  • Discuss integration opportunities for optimizing surgical care and detecting complications.

Main Methods:

  • Review of AI and ML applications in perioperative care.
  • Analysis of current predictive models for surgical patients.
  • Discussion of implementation strategies and limitations.

Main Results:

  • AI and ML can refine existing tools and create new ones for perioperative outcomes.
  • Predictive models show potential for optimizing patient selection and care.
  • Early detection of postoperative complications is a key opportunity.

Conclusions:

  • AI and ML offer significant potential to improve perioperative patient care.
  • Integration into clinical workflows requires careful consideration of limitations.
  • Further research and development are needed for widespread adoption.