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FAST-framework for AI-based surgical transformation.

Harmehr Sekhon1,2, Farid Al Zoubi3, Paul E Beaulé4

  • 1Division of Geriatric Medicine, Department of Medicine, St. Mary's Research Centre, McGill University, Montreal, QC, Canada.

Frontiers in Big Data
|September 29, 2025
PubMed
Summary
This summary is machine-generated.

The Framework for AI-based Surgical Transformation (FAST) uses machine learning to provide real-time recommendations, significantly improving surgical success rates (SSR) and operating room efficiency. This innovative model enhances patient outcomes and resource utilization in healthcare.

Keywords:
artificial intelligenceclinical translationoperating roomprescriptive analyticssurgical data science

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Area of Science:

  • Artificial Intelligence in Medicine
  • Machine Learning Applications
  • Surgical Workflow Optimization

Background:

  • Traditional machine learning (ML) in surgery primarily focuses on prediction, yielding limited improvements in operating room (OR) efficiency and surgical success rates (SSR).
  • Increasing surgical wait times and healthcare resource constraints necessitate innovative ML solutions for real-time OR optimization.
  • The Framework for AI-based Surgical Transformation (FAST) was developed to address these challenges by providing immediate recommendations to enhance OR efficiency.

Purpose of the Study:

  • To develop and evaluate the FAST model for real-time recommendations to improve OR efficiency.
  • To assess the impact of FAST on surgical success rates (SSR) in orthopedic surgeries.
  • To determine the feasibility and implementability of the FAST framework in a clinical setting.

Main Methods:

  • The FAST model was developed using six ML algorithms, including decision trees and neural networks.
  • Evaluation involved a dataset of 4796 orthopedic cases, incorporating surgery and team-specific variables.
  • Regular stakeholder seminars were conducted to ensure adherence and uptake of the FAST system.

Main Results:

  • FAST demonstrated feasibility and implementability in orthopedic ORs, with strong team engagement.
  • The implementation led to a significant increase in SSR from 39% at baseline to 93% over 23 weeks.
  • Key factors influencing SSR included timely initiation of the first surgery, efficient OR turnover, and optimal team composition.

Conclusions:

  • FAST is a novel ML framework capable of delivering real-time feedback to enhance OR efficiency and SSR.
  • Successful integration of stakeholders is crucial for the adoption and effectiveness of the FAST system.
  • The FAST framework offers a versatile and innovative ML application for improving OR efficiency across various hospital settings and surgical procedures without requiring additional resources.