English(EN) PrivMeSA: Privacy-Aware Self-Evolving Multi-Agent System for Medicine via Local-Remote LLM Collaboration

新AI系统PrivMeSA在保护患者隐私的同时增强了医疗LLM咨询

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了PrivMeSA，一个新颖的多智能体系统，旨在利用大型语言模型（LLMs）增强医疗咨询，同时保护患者隐私。该系统允许本地智能体通过学习控制敏感信息的披露来咨询更强大的远程LLM。通过强化学习，PrivMeSA在任务准确性和重新识别风险之间取得平衡，显著降低了直接个人信息披露的案例百分比以及注册表中潜在患者匹配的数量。该系统还包含一个本地内存，用于提炼和重用过去咨询的专业知识，使后续案例能够在不进一步传输数据的情况下受益于先前的远程交流。 AI

影响该系统通过解决隐私问题，可能有助于在医疗保健等敏感领域更安全地部署LLM。

排序理由该集群描述了一个在arXiv学术论文中提出的新颖系统。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群描述了一个在arXiv学术论文中提出的新颖系统。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Dannong Wang, Yuran Zhang, Bian Sun, Alex Stinard, Yuzhang Shang, Song Wang, Yu Tian · 2026-10-01 04:00

PrivMeSA：用于医疗的隐私感知自演化多智能体系统，通过本地-远程LLM协作实现

arXiv:2609.38458v1 Announce Type: new Abstract: Clinical large language model (LLM) agents deployed locally can consult more capable remote models, but doing so risks exposing patient information. Privacy-conscious delegation places disclosure decisions with a local agent, yet re…

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报道来源 [1]

PrivMeSA：用于医疗的隐私感知自演化多智能体系统，通过本地-远程LLM协作实现

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