arXiv:2606.02276v1 Announce Type: cross Abstract: 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 image-to-report retrieval and used public paired cohor
The increasing deployment of advanced vision-language models in clinical settings raises immediate concerns regarding data privacy and security, as these models can inadvertently re-identify de-identified patient data.
This highlights a critical and emerging privacy risk in medical AI, particularly concerning patient data sharing and anonymization practices, which could impact regulatory frameworks and public trust.
The understanding of clinical VLM privacy risks expands beyond traditional identification methods, necessitating new approaches to de-identification and data governance for shared medical imaging and reports.
- · Privacy-enhancing technology developers
- · Cybersecurity firms specializing in healthcare AI
- · Regulatory bodies focused on data governance
- · Hospitals and clinics with legacy data sharing policies
- · AI developers not prioritizing privacy-by-design
- · Patients whose de-identified data could be re-linked
Clinical deployment models will require more robust privacy-preserving mechanisms.
New standards for medical data anonymization specific to multimodal AI will emerge, impacting research and development.
Public distrust in AI-driven healthcare could increase if these privacy risks are not effectively mitigated, potentially slowing adoption.
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