Implementing trust in non-small cell lung cancer diagnosis with a conformalized uncertainty-aware AI framework (opens in new tab)

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 t...