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Can surgeons trust AI? Perspectives on machine learning in surgery and the importance of eXplainable Artificial
Johanna M Brandenburg1,2, Beat P Müller-Stich2,3, Martin Wagner4,5,6
1Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.
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.
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