Prof. ZHANG Xiaoge, Member of Research Institute for Advanced Manufacturing (RIAM), Research Institute for Artificial Intelligence of Things (RIAIoT) and Otto Poon Charitable Foundation Research Institute for Smart Energy (RISE), Assistant Professor of Department of Industrial and Systems Engineering, and his research team have developed an integrated AI framework named TRUECAM (TRustworthiness-focused, Uncertainty-aware, End-to-end CAncer diagnosis with Model-agnostic capabilities). Applied to whole-slide image analysis for non-small cell lung cancer subtyping, the framework ensures both data and model trustworthiness, laying an important foundation for the safe application of pathology AI in cancer diagnosis.
TRUECAM is designed to enhance the reliability of AI-assisted cancer diagnosis. It can assess the level of the AI’s confidence in its diagnostic outputs and proactively prompt pathologists to review cases when uncertainty is high or when input data falls outside the model’s scope. At the same time, the framework is model-agnostic, supporting the complete analysis pipeline from pathology images to diagnostic outcomes and helping healthcare professionals apply AI-powered diagnoses more reliably.
The framework is applied to whole-slide imaging, a process that digitally scans glass tissue slides into super-resolution digital images and provides “virtual microscopy”, allowing pathologists to review pathology samples on a computer. The findings show that TRUECAM is applicable not only to non-small cell lung cancer subtyping, but can also be extended to breast, brain and kidney cancer subtyping tasks, as well as a 46-class pan-cancer slide-level classification setting, demonstrating its broad application potential and clinical translational value.
As a general framework, TRUECAM can be integrated into pathology AI models of various sizes, architectures, purposes and complexities to support responsible clinical applications. The framework has three core functions: detecting out-of-scope inputs, automatically eliminating highly ambiguous and difficult-to-judge image regions, and applying conformal prediction to keep the diagnostic error rates within an acceptable range.
The research team conducted a systematic evaluation of the framework across multiple cancer datasets using two types of AI models: specialised models designed for particular tasks and foundation models with broad application potential. Their computational experiments indicate that TRUECAM-wrapped models consistently outperformed their unwrapped counterparts in classification accuracy, robustness, interpretability, data efficiency and fairness.
This research has been published in the internationally renowned journal Nature Biomedical Engineering, and received funding support from the National Natural Science Foundation of China, the Research Grants Council of the Hong Kong Special Administrative Region, and the Shenzhen Science and Technology Program.
Read the full paper: https://www.nature.com/articles/s41551-026-01694-8
Press release: https://www.polyu.edu.hk/en/media/media-releases/2026/0818_polyu-research-team-develops-trustworthy-ai-framework-truecam/
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| Research Units | Research Institute for Advanced Manufacturing | Research Institute for Artificial Intelligence of Things | Otto Poon Charitable Foundation Research Institute for Smart Energy |
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