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Liquid white box model as an explainable AI for surgery.

Homer A Riva-Cambrin1, Rahul Singh1, Sanju Lama1

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Summary
This summary is machine-generated.

This study introduces explainable artificial intelligence (AI) models for real-time surgical data analysis, improving surgeon feedback and training. The AI models enhance surgical safety and standardization through transparent decision-making processes.

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Area of Science:

  • Medical Artificial Intelligence
  • Surgical Informatics
  • Machine Learning in Healthcare

Background:

  • Real-time surgical data analysis is crucial for enhancing surgeon feedback, learning, and performance.
  • Data-driven systems promise safer, more standardized surgeries and accelerated training.
  • Artificial intelligence (AI) can address limitations in human and classical computing for efficient information processing.

Purpose of the Study:

  • To develop explainable AI models for surgical task and skill classification.
  • To provide transparent explanations for AI-driven surgical decision-making.
  • To investigate the use of liquid time constant models for improved performance under constraints.

Main Methods:

  • Development of two distinct AI models: one for surgical task classification and another for skill classification.
  • Implementation of explainability features to elucidate model decision processes.
  • Investigation of liquid time constant models for enhanced performance and interpretability.

Main Results:

  • Successfully created AI models capable of classifying surgical tasks and skills.
  • Demonstrated the models' ability to predict and explain surgical decisions.
  • Showcased the effectiveness of liquid time constant models in constrained environments.

Conclusions:

  • Explainable AI models can significantly improve real-time surgical data understanding and application.
  • Transparent AI decision-making is essential for robust model development and adoption in surgery.
  • Liquid time constant models offer a promising approach for developing effective and interpretable AI in surgical contexts.