Large Language Models (LLMs) have shown impressive capabilities in generating human-like responses. However, their lack of domain-specific knowledge limits their applicability in healthcare settings, where contextual and comprehensive responses are vital. To address this challenge and enable the generation of patient-centric responses that are contextually relevant and comprehensive, we propose MedInsight:a novel retrieval augmented framework that augments LLM inputs (prompts) with relevant background information from multiple sources. MedInsight extracts pertinent details from the patient's medical record or consultation transcript. It then integrates information from authoritative medical textbooks and curated web resources based on the patient's health history and condition. By constructing an augmented context combining the patient's record with relevant medical knowledge, MedInsight generates enriched, patient-specific responses tailored for healthcare applications such as diagnosis, treatment recommendations, or patient education. Experiments on the MTSamples dataset validate MedInsight's effectiveness in generating contextually appropriate medical responses. Quantitative evaluation using the Ragas metric and TruLens for answer similarity and answer correctness demonstrates the model's efficacy. Furthermore, human evaluation studies involving Subject Matter Expert (SMEs) confirm MedInsight's utility, with moderate inter-rater agreement on the relevance and correctness of the generated responses.

翻译：大语言模型（LLMs）在生成类人响应方面展现出令人瞩目的能力。然而，由于缺乏领域特定知识，其在医疗场景中的应用受限，而此类场景中上下文相关且全面的响应至关重要。为解决这一挑战并实现生成具有语境相关性与全面性的患者中心型响应，我们提出MedInsight：一种新颖的检索增强框架，通过从多源数据中提取相关背景信息来增强大语言模型输入（提示词）。MedInsight首先从患者的医疗记录或问诊记录中抽取关键细节，随后基于患者的健康史与病情，整合来自权威医学教科书及精选网络资源的信息。通过构建融合患者病历与相关医学知识的增强型上下文，该框架为医疗应用（如诊断、治疗建议或患者教育）生成富集化、个体化的患者响应。在MTSamples数据集上的实验验证了MedInsight在生成语境恰当医学响应方面的有效性。基于Ragas指标与TruLens对答案相似度及正确性的定量评估，证明了该模型的效能。此外，涉及领域专家（SMEs）的人工评估研究进一步证实了MedInsight的实用性，且评估者在生成响应的相关性与正确性方面具有中等水平的评分者间一致性。