CaseGPT: a case reasoning framework based on language models and retrieval-augmented generation

TL;DR

CaseGPT combines large language models with retrieval-augmented generation to improve case-based reasoning, enabling fuzzy searches and generating insightful recommendations in healthcare and legal domains.

Contribution

It introduces a novel framework that integrates LLMs with RAG technology for enhanced case reasoning and retrieval in complex, imprecise query scenarios.

Findings

Outperforms traditional keyword-based systems in precision and recall

Enhances data searchability and usability in healthcare and legal sectors

Demonstrates significant efficiency improvements in case retrieval

Abstract

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.