Masking Is Not Enough: Generative Restoration for Multimodal De-Identification in Medical AI

• URL: http://arxiv.org/abs/2608.21133v1
• Date: Fri, 21 Aug 2026 14:08:45 GMT
• Title: Masking Is Not Enough: Generative Restoration for Multimodal De-Identification in Medical AI
• Abstract summary: Medical image-text data can expose protected health information (PHI) through both visible image content and accompanying text.<n>We introduce ClinX, an end-to-end multimodal PHI sanitization framework for medical imagetext data.<n>We evaluate ClinX in medical visual question answering (MedVQA), jointly measuring PHI leakage and downstream utility across image-side, text-side, and combined de-identification settings.
• Score: 16.063105966676456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image-text data can expose protected health information (PHI) through both visible image content as well as accompanying text, creating a barrier to privacy-preserving medical AI systems. This risk is especially prominent in multimodal systems, where images, questions, reports, and clinical context may enter training, evaluation, or inference pipelines. Existing medical vision-language benchmarks primarily emphasize task utility, while de-identification methods are often evaluated separately from downstream reasoning. We introduce ClinX, an end-to-end multimodal PHI sanitization framework for medical image-text data. ClinX detects visible identifiers with optical character recognition (OCR), constructs binary PHI masks, and applies ClinX-PRISM, a no-skip generative restoration module with privacy-oriented post-processing for burned-in identifier suppression. In parallel, text-side PHI is reduced through progressive de-identification levels: regex masking, context-aware masking, and rewrite-based sanitization. We evaluate ClinX in medical visual question answering (MedVQA), jointly measuring PHI leakage and downstream utility across image-side, text-side, and combined de-identification settings. Results show that OCR-only masking is not sufficient as a standalone solution, and restoration-based sanitization better preserves clinically relevant visual context while sharply reducing recoverable PHI.

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