Liquid white box model as an explainable AI for surgery.

Journal: NPJ digital medicine
Published Date: Jun 19, 2025

Abstract

Understanding surgical data in real-time will lead to improved feedback, learning, and performance for surgeons. This is important as data-driven systems offer safer, more standardized surgery, and faster training times. Artificial intelligence shows great promise in filling the gap where humans and classical computing algorithms cannot process information in an efficient manner. Defining a true application and development of robust artificial intelligence models mandates that they be explainable and transparent in how they make decisions. In this work, we meet this need by creating two models for surgical task and skill classification, respectively, to predict and provide an explanation of how surgical decisions are made. We further investigate how models based on a liquid time constant can be effectively utilized to develop better models under constraints and explain how the model makes internal decisions.

Authors

  • Homer A Riva-Cambrin
    Project neuroArm, Dept. Of Clinical Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Rahul Singh
    Disease Investigation Laboratory, ICAR-Indian Veterinary Research Institute, Palampur, India.
  • Sanju Lama
    Project neuroArm, Health Research Innovation Center and Hotchkiss Brain Institute, Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Alberta, Canada.
  • Garnette R Sutherland
    Project neuroArm, Health Research Innovation Center and Hotchkiss Brain Institute, Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Alberta, Canada.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals