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Updated: Jul 12, 2026
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evaluation of a Retrieval-Augmented Generation-Based Large Language Model for Evidence-Based Herb and Supplement
Yen-Nien Hou1, Jyothirmai Gubili1, Pulkit Jain2
1Integrative Medicine and Wellness Service, Memorial Sloan Kettering Cancer Center, 321 E 61st Street, 4th Fl., New York, NY, 10065, United States, 1 6466088550.
JMIR Cancer
|July 9, 2026
Summary
This study developed a large language model using retrieval-augmented generation to enhance answers about herbs and dietary supplements for healthcare providers.
Area of Science:
- Artificial Intelligence
- Pharmacology
- Natural Products
Background:
- Healthcare providers frequently encounter complex questions regarding the efficacy and safety of herbs and dietary supplements.
- Existing information retrieval systems may struggle to synthesize comprehensive answers from diverse sources.
Purpose of the Study:
- To develop and evaluate a novel retrieval-augmented generation (RAG) large language model (LLM).
- To improve the accuracy and relevance of responses to clinical queries about herbal medicine and dietary supplements.
Main Methods:
- Implementation of a RAG framework integrating a large language model with an external knowledge base.
- Systematic evaluation of the model's response quality using a defined set of provider questions.
Main Results:
- The developed RAG-based LLM demonstrated improved response generation capabilities compared to baseline models.
- Qualitative and quantitative assessments indicated enhanced accuracy and comprehensiveness of the model's outputs.
Conclusions:
- Retrieval-augmented generation offers a promising approach for enhancing AI-driven clinical decision support tools.
- This LLM has the potential to aid healthcare providers in delivering evidence-based information on herbs and dietary supplements.
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