Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework

A comparative study has been conducted on optimizing medical question-answering systems using fine-tuned and zero-shot large language models (LLMs) within a retrieval-augmented generation (RAG) framework. The research highlights the challenges of applying LLMs in clinical settings, particularly in maintaining factual accuracy and minimizing hallucinations. The study demonstrates that fine-tuning models like LLaMA~2 and Falcon significantly improves answer accuracy by integrating domain-specific knowledge retrieval.
This development is crucial for enhancing the reliability of medical QA systems, which are increasingly relied upon for accurate information in clinical decision-making. By grounding LLM responses in relevant medical literature, the study aims to address the critical need for factual correctness in healthcare applications, thereby potentially improving patient outcomes and trust in AI-assisted medical tools.
The findings resonate with ongoing discussions in the AI community regarding the balance between model performance and ethical considerations, such as privacy and data integrity. Innovations like the hierarchical dual-strategy for knowledge unlearning and frameworks for reinforcement learning in medical diagnostics reflect a broader trend towards refining AI applications in sensitive domains, emphasizing the importance of responsible AI development in healthcare.

Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework