为病理AI可解释性提供术语、分类与临床应用框架
Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations
- 构建7个病理导向的核心术语体系
- 提出跨阶段/类型/范围的三轴分类法
- 针对5类临床问题推荐适配方法与评估路径
计算病理学（CompPath）正通过人工智能算法分析千兆像素级全切片图像，支持诊断、预后和治疗预测。尽管临床应用逐步推进，但在高风险医疗环境中仍受限于安全、问责与监管难题。可解释人工智能（XAI）有望提升可信度并支持验证，但现有文献因术语不一致、方法重叠、验证随意而碎片化。本文旨在通过三项举措实现规范化：一、提出包含七个核心术语的病理中心术语体系；二、建立涵盖方法家族及三个正交维度（阶段、类型、范围）的分类框架；三、构建以任务为导向的体系，将五类临床问题映射至推荐方法、评估方式与部署场景。识别出当前XAI能力与临床落地间的五大差距，并提出可操作改进路径。
原文摘要 · Abstract (English)
Computational pathology (CompPath) is transforming medicine by leveraging artificial intelligence (AI) algorithms to support diagnosis, prognosis, and treatment prediction from gigapixel whole-slide images. Clinical adoption is progressing, but is constrained by concerns about safety, accountability, and regulatory oversight in high-stakes clinical environments. Explainable AI (XAI) systems hold promise for building trust and enabling verification, yet the literature remains fragmented due to inconsistent terminology, overlapping methodological families, ad hoc validation, and current reviews. This review aims to formalize XAI methods in CompPath through the: i) introduction of a pathology-centric vocabulary comprising seven core terms; ii) development of a taxonomy across methodological families and three orthogonal axes (stage, type, scope); and iii) establishment of a task-driven framework that maps five clinical questions to recommended methods, method evaluation, and deployment context. Five key gaps between current XAI capabilities and clinical deployment are identified, and actionable steps are proposed to advance XAI for CompPath.
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