Brandenburg Johanna M, Müller-Stich Beat P, Wagner Martin, van der Schaar Mihaela
Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.
National Center for Tumor Diseases (NCT), Heidelberg, Germany.
Langenbecks Arch Surg. 2025 Jan 28;410(1):53. doi: 10.1007/s00423-025-03626-7.
This brief report aims to summarize and discuss the methodologies of eXplainable Artificial Intelligence (XAI) and their potential applications in surgery.
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.
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.
Transparency and interpretability are essential for the effective integration of AI models into clinical practice.
本简要报告旨在总结和讨论可解释人工智能（XAI）的方法及其在手术中的潜在应用。
我们简要介绍可解释性方法，包括全局和个体解释特征、成像数据和时间序列的方法，以及相似性分类、解开的规则和规律。
鉴于手术领域对人工智能的兴趣日益增加，我们强调应用模型输出的透明度和可解释性至关重要。
透明度和可解释性对于将人工智能模型有效整合到临床实践中至关重要。
