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.

The authors have declared no competing interest.

X.Z. and T.W. were partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU 25206422) and the National Natural Science Foundation of China (Grant No. 62406269). C.Y. was supported by the U.S. National Institutes of Health (NIH) NLM Pathway to Independence Award 1K99LM014428-01A1.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

TCGA, CPTAC

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The TCGA diagnostic whole-slide data and corresponding labels are available from the NIH genomic data commons https://portal.gdc.cancer.gov. The CPTAC whole-slide data and the corresponding labels are available from the NIH cancer imaging archive https://cancerimagingarchive.net/datascope/cptac.

https://portal.gdc.cancer.gov

https://cancerimagingarchive.net/datascope/cptac