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

Abstract

BackgroundLarge language models (LLMs) have significant capabilities in clinical information processing tasks. Commercially available LLMs, however, are not optimized for clinical uses and are prone to generating incorrect or hallucinatory information. Retrieval-augmented generation (RAG) is an enterprise architecture that allows embedding of customized data into LLMs. This approach “specializes” the LLMs and is thought to reduce hallucinations.MethodsWe developed “LiVersa,” a liver disease-specific LLM, by using our institution’s protected health information (PHI)-complaint text embedding and LLM platform, “Versa.” We conducted RAG on 30 publicly available American Association for the Study of Liver Diseases (AASLD) guidelines and guidance documents to be incorporated into LiVersa. We evaluated LiVersa’s performance by comparing its responses versus those of trainees from a previously published knowledge assessment study regarding hepatitis B (HBV) treatment and hepatocellular carcinoma (HCC) surveillance.ResultsLiVersa answered all 10 questions correctly when forced to provide a “yes” or “no” answer. Full detailed responses with justifications and rationales, however, were not completely correct for three of the questions.DiscussionsIn this study, we demonstrated the ability to build disease-specific and PHI-compliant LLMs using RAG. While our LLM, LiVersa, demonstrated more specificity in answering questions related to clinical hepatology – there were some knowledge deficiencies due to limitations set by the number and types of documents used for RAG. The LiVersa prototype, however, is a proof of concept for utilizing RAG to customize LLMs for clinical uses and a potential strategy to realize personalized medicine in the future.

Applications of Large Language Models in Pathology

“…LLMs can produce hallucinations [99] and recommend products/tools that may not exist. Retrieval augmented generation (RAG) methods offer promise in mitigating some of these deficiencies [100]. RAG comprises a set of techniques and methodologies that enable a large language model to query an external data source, potentially improving the accuracy and relevance of responses while reducing the incidence of hallucinations and inappropriate responses [101].…”