Protecting patient privacy in clinical foundation models: Technical and legal perspectives

2026-08-07

AI Summary

This work addresses the underexplored risk of indirect patient privacy leakage through deployed clinical foundation models, a challenge inadequately mitigated by current legal and technical safeguards. It proposes, for the first time, a context-aware privacy risk assessment framework that systematically integrates technical and legal perspectives to evaluate indirect leakage risks across the model’s entire lifecycle. By combining privacy risk modeling, leakage scenario simulation, and compliance mapping with technical measures such as differential privacy and access control, the framework elucidates representative leakage mechanisms. It delivers an actionable risk assessment workflow and cross-jurisdictional compliance guidance, enabling robust privacy protection without compromising model utility.

Abstract

Clinical foundation models trained on large-scale patient data are increasingly used for decision support, screening, and public health. As deployment expands, privacy risk increasingly arises from model-mediated leakage, yet its prevalence and severity remain poorly quantified. Models can disclose sensitive training artifacts, enabling patient re-identification in ways not captured by data-handling controls alone. Existing frameworks, including HIPAA and GDPR, offer limited guidance for such indirect threats. We propose a practical framework for assessing privacy risk in clinical foundation models and illustrate realistic leakage scenarios across deployment settings, map them to legal regimes, and outline complementary technical and legal mitigations. Our analysis provides a context-aware risk assessment grounded in realistic usage to preserve the value of medical foundation models while rigorously safeguarding patient privacy.

Problem

• clinical foundation models
• privacy risk
• patient re-identification
• model-mediated leakage
• data privacy

Innovation

• clinical foundation models
• privacy risk assessment
• model-mediated leakage
• patient re-identification
• technical-legal mitigation