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

arXiv: https://arxiv.org/abs/2407.07913 · HTML: https://mlsanity.com/paper/2407.07913/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)
- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)

## Built on by (most cited first, 7 of 7)

- [A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents](https://mlsanity.com/paper/2602.06052/) (2026)
- [LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning](https://mlsanity.com/paper/2605.28120/) (2026)
- [Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interaction](https://mlsanity.com/paper/2505.23034/) (2025)
- [GAIus: Combining Genai with Legal Clauses Retrieval for Knowledge-based Assistant](https://mlsanity.com/paper/2507.01259/) (2025)
- [A General Retrieval-Augmented Generation Framework for Multimodal Case-Based Reasoning Applications](https://mlsanity.com/paper/2501.05030/) (2025)
- [Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents](https://mlsanity.com/paper/2604.27143/) (2026)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)

## Abstract (arXiv)

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.