Reliably processing and interlinking medical information has been recognized as a critical foundation to the digital transformation of medical workflows, and despite the development of medical ontologies, the optimization of these has been a major bottleneck to digital medicine. The advent of large language models has brought great excitement, and maybe a solution to the medicines’ ‘communication problem’ is in sight, but how can the known weaknesses of these models, such as hallucination and non-determinism, be tempered? Retrieval Augmented Generation, particularly through knowledge graphs, is an automated approach that can deliver structured reasoning and a model of truth alongside LLMs, relevant to information structuring and therefore also to decision support.

中文翻译：

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增强的非幻觉大型语言模型作为医疗信息管理者

可靠地处理和互连医疗信息已被认为是医疗工作流程数字化转型的关键基础，尽管医学本体不断发展，但其优化一直是数字医学的主要瓶颈。大型语言模型的出现带来了极大的兴奋，也许药物“沟通问题”​​的解决方案就在眼前，但如何缓和这些模型已知的弱点，例如幻觉和非决定论？检索增强生成，特别是通过知识图，是一种自动化方法，可以与法学硕士一起提供结构化推理和真理模型，与信息结构化相关，因此也与决策支持相关。