Framework for Patient Privacy in Clinical Foundation Models
Key takeaways
- Clinical foundation models pose new privacy risks from model-mediated data leakage.
- Existing privacy laws offer limited guidance for these indirect threats.
- A new framework assesses privacy risk, maps leakage scenarios to legal regimes, and proposes mitigations.
- Combining technical and legal strategies is crucial for safeguarding patient privacy in medical AI.
Who benefits
Summary
This paper proposes a practical framework for assessing privacy risk in clinical foundation models, addressing model-mediated data leakage that current legal frameworks like HIPAA and GDPR inadequately cover. It illustrates realistic leakage scenarios, maps them to legal regimes, and outlines complementary technical and legal mitigations to safeguard patient privacy while preserving the value of medical foundation models.
Why it matters
For healthcare organizations, AI developers, and legal professionals, understanding and mitigating privacy risks in clinical foundation models is critical for ethical deployment, regulatory compliance, and maintaining patient trust. This framework provides essential guidance.
How to implement this in your domain
- 1Conduct a comprehensive privacy risk assessment for all clinical foundation models in development or deployment, focusing on model-mediated leakage.
- 2Map identified leakage scenarios to relevant legal frameworks (e.g., HIPAA, GDPR, EU AI Act) to ensure compliance.
- 3Implement technical mitigations such as differential privacy, federated learning, or confidential computing during model training and inference.
- 4Develop clear legal and organizational policies that address indirect privacy threats posed by AI models.
- 5Regularly audit and monitor clinical foundation models for potential privacy breaches and update mitigation strategies as needed.
Original post by Sana Tonekaboni, Lena Stempfle, Sasha Ronaghi, Corinna Coupette, I. Glenn Cohen, Emily Alsentzer, Marzyeh Ghassemi
"arXiv:2608.07705v1 Announce Type: new 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 pr…"
View on XOriginally posted by Sana Tonekaboni, Lena Stempfle, Sasha Ronaghi, Corinna Coupette, I. Glenn Cohen, Emily Alsentzer, Marzyeh Ghassemi on X · view source
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