Implementing trust in non-small cell lung cancer diagnosis with a conformalized uncertainty-aware AI framework

Ensuring trustworthiness is fundamental in cancer diagnostics, where a misdiagnosis can have dire consequences. Current pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. Here we introduce TRUECAM (Trustworthiness-focused, Uncertainty-aware, End-to-end Cancer diagnosis with Model-agnostic capabilities), a framework designed to ensure both data and model trustworthiness for 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, (2) an ambiguity-guided tile elimination to filter out highly ambiguous regions, addressing data trustworthiness, and (3) conformal prediction to ensure controlled error rates. We systematically evaluated TRUECAM across multiple cancer datasets using both task-specific and foundation models. Computational experiments suggest that models wrapped with TRUECAM consistently outperformed their unwrapped counterparts in classification accuracy, robustness, interpretability, data efficiency and fairness. These findings establish TRUECAM as a versatile framework for the responsible deployment of pathology AI in real-world settings.