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Development of a Liver Disease-Specific Large Language Model Chat Interface using Retrieval Augmented Generation.

Jin Ge¹, Steve Sun², Joseph Owens²

¹Division of Gastroenterology and Hepatology, Department of Medicine, University of California - San Francisco, San Francisco, CA.

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|November 21, 2023

Summary

We developed LiVersa, a liver disease-specific large language model (LLM) using retrieval-augmented generation (RAG). This specialized LLM shows promise for clinical applications, though further refinement is needed.

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Area of Science:

Artificial Intelligence in Medicine
Clinical Natural Language Processing
Biomedical Informatics

Background:

Commercial large language models (LLMs) lack clinical optimization and may generate inaccurate information.
Retrieval-augmented generation (RAG) enhances LLMs by incorporating custom data, aiming to reduce hallucinations.
Developing specialized LLMs is crucial for reliable clinical information processing.

Approach:

Created "LiVersa," a liver disease-specific LLM using a Protected Health Information (PHI)-compliant platform.
Employed RAG to integrate 30 American Association for the Study of Liver Diseases (AASLD) guidelines into LiVersa.
Evaluated LiVersa's performance against human trainees on hepatitis B and hepatocellular carcinoma knowledge assessments.

Key Points:

LiVersa achieved 100% accuracy in binary (yes/no) responses for clinical questions.
Detailed explanations from LiVersa were not fully accurate for three questions.
The study demonstrates the feasibility of creating disease-specific, PHI-compliant LLMs via RAG.

Conclusions:

LiVersa serves as a proof-of-concept for RAG-customized clinical LLMs.
Disease-specific LLMs can enhance specificity in clinical domains like hepatology.
This approach offers a potential pathway toward personalized medicine through tailored AI tools.