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Updated: Jul 11, 2025

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Machine learning models to predict surgical case duration compared to current industry standards: scoping review.

Christopher Spence1, Owais A Shah1, Anna Cebula1

  • 1Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK.

BJS Open
|November 6, 2023
PubMed
Summary

Artificial intelligence (AI) models, particularly neural networks, show improved accuracy in predicting surgical case durations compared to current standards. This advancement could help reduce surgical waiting lists and improve operating theatre efficiency.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Surgical Workflow Optimization

Background:

  • The COVID-19 pandemic has significantly increased surgical waiting lists in the UK.
  • Optimal operating theatre scheduling requires accurate surgical case duration predictions.
  • Current methods for predicting surgical duration lack accuracy.

Purpose of the Study:

  • To evaluate if Artificial Intelligence (AI) is more accurate than industry standards for predicting surgical case duration.
  • To analyze potential efficiency savings from implementing AI prediction models.

Main Methods:

  • A systematic literature search was conducted across PubMed, Embase, and MEDLINE up to July 2023.
  • The review followed PRISMA extension for scoping reviews and the Arksey and O'Malley framework.
  • Study quality was assessed using modified Farrow et al. guidelines, and algorithm performance was evaluated using standard metrics.

Main Results:

  • Out of 2593 identified articles, 14 were included; 13 compared AI accuracy against industry standards.
  • Seven studies demonstrated statistically significant improvements in prediction accuracy with AI (P < 0.05).
  • Neural networks showed superiority over other machine learning techniques; efficiency savings were noted in one RCT, though methodological limitations were common.

Conclusions:

  • Machine learning and deep learning models offer enhanced accuracy in predicting surgical case durations.
  • Further research is needed to optimize the implementation of these AI technologies in clinical practice.