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Explaining the global decision logic of black-box medical artificial intelligence (AI) models is a formidable challenge. We proposed class-association manifold learning, an explanatory framework that harnesses low-dimensional manifolds to visualize and accurately explore hidden global decision rules captured by medical AI models. Class-association manifold learning enabled human-interpretable medical knowledge discovery while ensuring AI model alignment.
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References
Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023). A review article on how AI will reshape scientific discovery.
Kundu, S. AI in medicine must be explainable. Nat. Med. 27, 1328 (2021). A viewpoint article that emphasized the importance of explainability in medical AI.
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Ghassemi, M., Oakden-Rayner, L. & Beam, A. L. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health 3, e745–e750 (2021). This paper illustrated the problem of ‘interpretability gaps’, providing criticism of current xAI.
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This is a summary of: Xie, R. et al. Bridging the interpretability gap for medical artificial intelligence models using class-association manifold learning. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-026-01676-w (2026).
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Translating black-box medical AI models into interpretable global decision logic. Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-026-01675-x
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DOI: https://doi.org/10.1038/s41551-026-01675-x
