标题： 使用一致性不确定感知人工智能框架在全片图像中实现非小细胞肺癌诊断中的信任 显示英文标题

标题： Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images

摘要： 确保可信性对于被视为社会负责任的人工智能（AI）的发展至关重要，特别是在癌症诊断领域，误诊可能带来严重后果。 当前的数字病理学AI模型缺乏系统性的解决方案来解决因模型局限性和部署与开发环境之间数据差异而产生的可信性问题。 为了解决这一问题，我们开发了TRUECAM框架，旨在确保非小细胞肺癌亚型分类中全片图像的数据和模型可信性。 TRUECAM集成了1）光谱归一化的神经高斯过程，用于识别超出范围的输入；2）基于模糊性引导的瓦片消除，过滤掉高度模糊的区域，从而解决数据可信性问题；以及3）一致性预测，以确保受控的错误率。 我们在多个大规模癌症数据集上系统地评估了该框架，利用任务特定模型和基础模型，表明被TRUECAM包裹的AI模型在分类准确性、鲁棒性、可解释性和数据效率方面显著优于缺乏此类指导的模型，同时在公平性方面也有所提升。 这些发现强调了TRUECAM作为数字病理学AI模型的多功能包装框架的重要性，促进了其在现实世界中的负责任和有效应用。 显示英文摘要

摘要： Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. To address this issue, we developed TRUECAM, a framework designed to ensure both data and model trustworthiness in non-small cell lung cancer subtyping with whole-slide images. TRUECAM integrates 1) a spectral-normalized neural Gaussian process for identifying out-of-scope inputs and 2) an ambiguity-guided elimination of tiles to filter out highly ambiguous regions, addressing data trustworthiness, as well as 3) conformal prediction to ensure controlled error rates. We systematically evaluated the framework across multiple large-scale cancer datasets, leveraging both task-specific and foundation models, illustrate that an AI model wrapped with TRUECAM significantly outperforms models that lack such guidance, in terms of classification accuracy, robustness, interpretability, and data efficiency, while also achieving improvements in fairness. These findings highlight TRUECAM as a versatile wrapper framework for digital pathology AI models with diverse architectural designs, promoting their responsible and effective applications in real-world settings.