Custom Large Language Models Improve Accuracy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Noncustom Models for Evidence-Based Medicine
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View abstract on PubMed
Summary
This summary is machine-generated.Customizing Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) and AI agents significantly boosts accuracy for medical information, outperforming standard LLMs in orthopaedic cases.
Area Of Science
- Medical Informatics
- Artificial Intelligence in Medicine
- Orthopaedic Surgery
Background
- Large Language Models (LLMs) show promise in medicine but face skepticism due to variable accuracy.
- Standard LLMs may not consistently provide reliable medical information.
- Customization techniques are needed to enhance LLM accuracy for clinical applications.
Purpose Of The Study
- To evaluate the efficacy of Retrieval Augmented Generation (RAG) and Agentic Augmentation in improving LLM accuracy.
- To compare custom LLM methods against standard LLMs using an anterior cruciate ligament (ACL) injury case.
- To demonstrate the value of tailored LLMs for delivering precise medical information.
Main Methods
- 100 ACL-related questions and answers based on 2022 AAOS guidelines were curated.
- Standard LLMs (closed-source and open-source) were tested, then augmented with RAG and AI agents.
- Fellowship-trained surgeons blindly assessed response accuracy; ROUGE and MEAT scores were calculated.
Main Results
- Non-custom LLMs performed below 60% accuracy.
- RAG improved accuracy by an average of 39.7% across all models.
- Meta's Llama3 70b (RAG-only) achieved 94% accuracy; OpenAI's GPT-4 (RAG + AI agents) reached 95%.
Conclusions
- Retrieval Augmented Generation (RAG) significantly enhances LLM accuracy in medical contexts.
- Agentic augmentation further refines LLM performance, achieving near-perfect accuracy.
- Customized LLMs, like RAG- and agent-augmented models, can serve as reliable sources for orthopaedic information, supporting patient decision-making.
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