This paper presents CaseGPT, an innovative approach that combines Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technology to enhance case-based reasoning in the healthcare and legal sectors. The system addresses the challenges of traditional database queries by enabling fuzzy searches based on imprecise descriptions, thereby improving data searchability and usability. CaseGPT not only retrieves relevant case data but also generates insightful suggestions and recommendations based on patterns discerned from existing case data. This functionality proves especially valuable for tasks such as medical diagnostics, legal precedent research, and case strategy formulation. The paper includes an in-depth discussion of the system's methodology, its performance in both medical and legal domains, and its potential for future applications. Our experiments demonstrate that CaseGPT significantly outperforms traditional keyword-based and simple LLM-based systems in terms of precision, recall, and efficiency.

翻译：本文提出CaseGPT，一种结合大型语言模型（LLM）与检索增强生成（RAG）技术的创新方法，旨在增强医疗与法律领域的案例推理能力。该系统通过支持基于模糊描述的检索，解决了传统数据库查询的局限性，从而提升了数据可检索性与可用性。CaseGPT不仅能检索相关案例数据，还能基于现有案例数据中识别的模式生成具有洞察力的建议与推荐。此功能在医疗诊断、法律判例研究与案例策略制定等任务中尤为宝贵。本文深入探讨了该系统的方法论、其在医疗与法律领域的表现以及未来应用潜力。实验表明，CaseGPT在精确率、召回率与效率方面均显著优于基于传统关键词的检索系统及简单的LLM基线系统。