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Purpose <p>This brief report aims to summarize and discuss the methodologies of eXplainable Artificial Intelligence (XAI) and their potential applications in surgery.</p> Methods <p>We briefly introduce explainability methods, including global and individual explanatory features, methods for imaging data and time series, as well as similarity classification, and unraveled rules and laws.</p> Results <p>Given the increasing interest in artificial intelligence within the surgical field, we emphasize the critical importance of transparency and interpretability in the outputs of applied models.</p> Conclusion <p>Transparency and interpretability are essential for the effective integration of AI models into clinical practice.</p>

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Can surgeons trust AI? Perspectives on machine learning in surgery and the importance of eXplainable Artificial Intelligence (XAI)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月28日

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  • Johanna M. Brandenburg,
  • Beat P. Müller-Stich,
  • Martin Wagner,
  • Mihaela van der Schaar

摘要

Purpose

This brief report aims to summarize and discuss the methodologies of eXplainable Artificial Intelligence (XAI) and their potential applications in surgery.

Methods

We briefly introduce explainability methods, including global and individual explanatory features, methods for imaging data and time series, as well as similarity classification, and unraveled rules and laws.

Results

Given the increasing interest in artificial intelligence within the surgical field, we emphasize the critical importance of transparency and interpretability in the outputs of applied models.

Conclusion

Transparency and interpretability are essential for the effective integration of AI models into clinical practice.