Cross-modal linkage risk in clinical vision-language models

Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shared embedding space that can preserve instance-level image-report correspondence. This poses a privacy risk in settings where radiographs and reports are deliberately kept separate after acquisition, such as image-only data sharing or access-controlled reports, because a de-identified image may be re-linked to its original narrative report through cosine similarity alone. We formalized this as imag

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ethics.ai (1 June 2026), “Cross-modal linkage risk in clinical vision-language models,” evidence record 3284, https://ethics.ai/record/3284 (originally published by arXiv).

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