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Article Abstract
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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11775030 | PMC |
| http://dx.doi.org/10.1007/s00423-025-03626-7 | DOI Listing |
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