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Rare disease diagnosis using knowledge guided retrieval augmentation for ChatGPT.

Charlotte Zelin¹, Wendy K Chung², Mederic Jeanne²

¹Blind Brook High School, Rye Brook, NY, USA.

Journal of Biomedical Informatics

|July 31, 2024

Summary

This summary is machine-generated.

RareDxGPT, an enhanced ChatGPT model, shows promise in rare disease diagnosis by integrating external knowledge. This AI tool achieved higher accuracy than standard ChatGPT, suggesting potential for improving diagnostic timelines.

Keywords:

Diagnostic Decision Support Generative AI Large Language Models Rare Disease

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Area of Science:

Artificial Intelligence in Medicine
Computational Diagnostics
Rare Disease Research

Background:

Rare diseases collectively impact millions globally, yet diagnosis is often delayed (average 5 years), leading to misdiagnosis or lack of diagnosis.
Machine learning (ML) has shown potential in aiding medical diagnostics, prompting investigation into advanced AI models for complex conditions like rare diseases.

Purpose of the Study:

To evaluate the diagnostic support capabilities of ChatGPT, enhanced with Retrieval Augmented Generation (RAG), for rare diseases.
To compare the accuracy of the enhanced model (RareDxGPT) against the base ChatGPT model across different prompting strategies.

Main Methods:

Developed RareDxGPT by integrating ChatGPT with the RareDis Corpus (717 rare diseases) using RAG to provide domain-specific context.
Extracted phenotypes for 30 rare diseases from PubMed case reports and tested both models using three prompt types: 'prompt', 'prompt + explanation', and 'prompt + role play'.

Main Results:

RareDxGPT demonstrated improved accuracy over ChatGPT 3.5 across all prompt types.
With 'Prompt + Explanation', RareDxGPT achieved 43% accuracy compared to ChatGPT 3.5's 23%.
With 'Prompt', RareDxGPT reached 40% accuracy versus ChatGPT 3.5's 37%.

Conclusions:

ChatGPT, particularly when augmented with domain-specific knowledge via RAG, shows significant early potential as a tool for rare disease diagnostic support.
Further refinements to AI models and prompting techniques could enhance diagnostic accuracy and reduce delays for rare disease patients.