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  1. Home
  3. Disparate Privacy Risks From Medical Ai.
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  3. Disparate Privacy Risks From Medical Ai.

Related Experiment Videos

Disparate privacy risks from medical AI.

Moritz A Knolle1, Martin J Menten2,3,4, Friederike Jungmann2,5

  • 1Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany. moritz.knolle@tum.de.

Nature
|June 24, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Medical artificial intelligence (AI) models pose privacy risks. Membership inference attacks (MIAs) can identify individual patient data in AI training sets, with higher risks for underrepresented groups. Aggregate metrics underestimate individual patient privacy exposure.

Related Experiment Videos

Area of Science:

  • Medical Artificial Intelligence
  • Data Privacy
  • Machine Learning Security

Background:

  • Medical AI models improve diagnostics but use sensitive patient data.
  • Existing privacy attack assessments are aggregate, not patient-level.
  • Individual patient privacy risks in AI training data are poorly understood.

Purpose of the Study:

  • To conduct patient-level privacy audits of AI diagnostic models.
  • To evaluate the effectiveness of membership inference attacks (MIAs) on individual patient data.
  • To identify disparities in privacy risks among different patient groups.

Main Methods:

  • Focused on membership inference attacks (MIAs).
  • Audited AI models across diverse medical datasets.
  • Analyzed attack success rates at the individual patient level.
  • Investigated the impact of model capacity and patient demographics on privacy risk.

    • Main Results:

    • MIAs achieved near-perfect success rates for individual patients.
    • High attack success rates increased with model capacity.
    • Underrepresented groups faced disproportionately higher attack success.
    • Aggregate privacy metrics significantly underestimated individual patient risk.

    Conclusions:

    • Patient-level privacy audits reveal substantial individual privacy risks in medical AI.
    • Aggregate privacy metrics are insufficient for assessing real-world privacy exposure.
    • Disparities in privacy risk highlight the need for equitable privacy protection.
    • Further research is needed for advanced risk assessment and mitigation strategies.