让医疗影像AI决策变透明，医生能看懂模型怎么想的。
Towards a Transparent and Interpretable AI Model for Medical Image Classifications
- 用多种医疗数据集模拟XAI模型，揭示其决策逻辑
- 验证XAI能有效解释AI预测，提升临床判断可信度
- 适合关注AI可解释性与医疗落地的研究者
人工智能在医学领域的应用前景广阔，但复杂模型的内在不透明性限制了其临床实用性。本文聚焦可解释人工智能（XAI）方法的应用研究，旨在使AI决策过程透明可理解。通过在多个医疗数据集上开展模拟实验，揭示XAI模型如何解析AI预测结果，从而增强医护人员对诊断决策的信任与理解。文章还系统梳理了主流XAI方法，并讨论当前领域面临的挑战。研究强调需持续探索适用于多样化医疗数据的XAI技术，以推动其在医疗场景中的有效落地与广泛应用。
原文摘要 · Abstract (English)
The integration of artificial intelligence (AI) into medicine is remarkable, offering advanced diagnostic and therapeutic possibilities. However, the inherent opacity of complex AI models presents significant challenges to their clinical practicality. This paper focuses primarily on investigating the application of explainable artificial intelligence (XAI) methods, with the aim of making AI decisions transparent and interpretable. Our research focuses on implementing simulations using various medical datasets to elucidate the internal workings of the XAI model. These dataset-driven simulations demonstrate how XAI effectively interprets AI predictions, thus improving the decision-making process for healthcare professionals. In addition to a survey of the main XAI methods and simulations, ongoing challenges in the XAI field are discussed. The study highlights the need for the continuous development and exploration of XAI, particularly from the perspective of diverse medical datasets, to promote its adoption and effectiveness in the healthcare domain.
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