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New framework reveals privacy risks in medical language models

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces a framework for evaluating the privacy risks of medical language models (LMs). The framework assesses leakage based on different levels of adversarial access, measuring both verbatim memorization of patient data and semantic disclosure of sensitive diagnoses. When applied to an LM trained on clinical notes, the study found significant risks of verbatim memorization of patient timelines and recovery of sensitive diagnoses, highlighting the dangers of training on longitudinal clinical data. AI

IMPACT Highlights potential privacy vulnerabilities in medical LMs, urging caution in training and deployment with sensitive patient data.

RANK_REASON Research paper published on arXiv detailing a new evaluation framework for medical LMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework reveals privacy risks in medical language models

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Research paper published on arXiv detailing a new evaluation framework for medical LMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sasha Ronaghi, Sana Tonekaboni, Lena Stempfle, Vivian Utti, Jordan Li Cahoon, Nathaniel Hendrix, Ayin Vala, Marzyeh Ghassemi, Emily Alsentzer ·

    Clinically Grounded Privacy Evaluation of Medical LMs

    arXiv:2606.09590v2 Announce Type: replace Abstract: Medical language models (LMs) can memorize and reproduce protected health information, but privacy evaluations often focus on recovery of training text rather than disclosure under realistic threat models. We introduce a clinica…

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