Medical LMs risk patient privacy, new evaluation framework shows

A new research paper introduces a privacy evaluation framework for medical language models, focusing on realistic threat models beyond simple text recovery. The framework assesses verbatim memorization and semantic leakage of sensitive diagnoses under varying levels of adversarial access. When applied to a model trained on clinical notes, it revealed high rates of memorization for encounter metadata and significant recovery of sensitive diagnoses like abortion and HIV, though some memorized tokens were templated. AI

IMPACT Highlights significant privacy risks in medical LMs, potentially influencing data handling and model development practices in healthcare AI.

RANK_REASON The cluster contains an academic paper detailing a new evaluation methodology for medical language models. [lever_c_demoted from research: ic=1 ai=1.0]

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• Medical LMs
• patient
• protected health information

• paper
• safety

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