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English(EN) Secure On-Premise Deployment of Open-Weights Large Language Models in Radiology: An Isolation-First Architecture with Prospective Pilot Evaluation

本地LLM架构使德国医院能够安全地部署放射科

研究人员开发并试点了一种隔离优先架构,用于在放射科内部安全地本地部署开源大语言模型。该系统旨在满足监管要求并处理未匿名化的受保护健康信息(PHI),通过严格的网络分段和监控来防止未经授权的外部访问。一项涉及22名放射科医生的试点研究表明,该系统在报告修正和指南推荐等任务方面具有良好的临床实用性,尽管开放式生成任务出现的关键错误频率较高。 AI

影响 为医疗保健等受监管环境中安全地本地部署LLM提供了蓝图,可能促进专业模型的广泛采用。

排序理由 学术论文,详细介绍了LLM部署的新颖架构及其试点评估。

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 →

本地LLM架构使德国医院能够安全地部署放射科

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学术论文,详细介绍了LLM部署的新颖架构及其试点评估。
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sebastian Nowak, Jann-Frederick La{\ss}, Narine Mesropyan, Babak Salam, Nico Piel, Mohammed Bahaaeldin, Wolfgang Block, Alois Martin Sprinkart, Julian Alexander Luetkens, Benjamin Wulff, Alexander Isaak ·

    放射科部署开源大语言模型:一种隔离优先架构的本地安全部署及前瞻性试点评估

    arXiv:2604.22768v1 Announce Type: cross Abstract: Purpose: To design, implement, evaluate, and report on the regulatory requirements of a self-hosted LLM infrastructure for radiology adhering to the principle of least privilege, emphasizing technical feasibility, network isolatio…