The interpretability paradox in cancer imaging and risk prediction: a critical narrative review of explainable AI, failure modes, and design alternatives
Deep learning has advanced cancer imaging and cancer-related risk prediction, but many high-performing models remain difficult to interrogate in clinically meaningful terms. This creates an interpretability paradox: gains in predictive performance often coincide with reduced transparency, while widely used post-hoc explanations can be persuasive without providing reliable evidence of model reasoning. Here, we present a critical narrative review and position argument, supported by a semi-systemat
Record details
Published: 7 July 2026
Source: Artificial Intelligence Review
Category: Research
Topics: Safety & alignment · Transparency
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (7 July 2026), “The interpretability paradox in cancer imaging and risk prediction: a critical narrative review of explainable AI, failure modes, and design alternatives,” evidence record 1982, https://ethics.ai/record/1982 (originally published by Artificial Intelligence Review).
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