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English(EN) Clinically Grounded Privacy Evaluation of Medical LMs

新框架揭示医疗语言模型的隐私风险

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文介绍了一个评估医疗语言模型（LMs）隐私风险的框架。该框架根据不同级别的对抗性访问来评估泄露情况，衡量患者数据的逐字记忆以及敏感诊断的语义披露。当应用于一个在临床笔记上训练的LM时，研究发现存在逐字记忆患者时间线和恢复敏感诊断的重大风险，凸显了在纵向临床数据上进行训练的危险性。 AI

影响 强调了医疗语言模型潜在的隐私漏洞，敦促在敏感患者数据上进行训练和部署时要谨慎。

排序理由 一篇发布在arXiv上的研究论文，详细介绍了医疗语言模型的新评估框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架揭示医疗语言模型的隐私风险

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一篇发布在arXiv上的研究论文，详细介绍了医疗语言模型的新评估框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [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 ·

    面向临床的医学大语言模型隐私评估

    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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