From Guidelines to Real-Time Conversation: Expert-Validated Retrieval-Augmented and Fine-Tuned GPT-4 for Hepatitis C Management.

ABSTRACT:

Background and aims

Advances in artificial intelligence, particularly large language models (LLMs), hold promise for transforming chronic disease management such as Hepatitis C Virus (HCV) infection. This study evaluates the impact of retrieval-augmented generation (RAG) and supervised fine-tuning (SFT) on both open-ended question answering (accuracy and clarity) and on LLM-recommended treatment regimens for clinical scenarios.

Methods

We employed OpenAI's GPT-4 Turbo in four configurations-baseline, RAG-Top1, RAG-Top 10 and SFT-using the 2020 EASL HCV guidelines as external knowledge or fine-tuning data. For the question set, guidelines were segmented at the paragraph level and encoded into 3072-dimensional embeddings. Fifteen questions covering general, patient and physici

SUBMITTER: Giuffre M 

PROVIDER: S-EPMC12442523 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature