Can Small Open-Source Language Models With Retrieval-Augmented Generation Match GPT-4 Performance in Breast Cancer Clinical Decision Support?

purposeThe rapidly evolving breast cancer treatment landscape creates significant information synthesis challenges for clinicians. We evaluated whether small open-source large language models (LLMs) augmented with retrieval-augmented generation (RAG) could match proprietary model performance for clinical guideline queries.

methodsWe developed a domain-specialized RAG pipeline using HTML-structure-preserving chunking of 1,356 ASCO breast cancer guideline documents. Five LLMs were each evaluated with and without RAG: GPT-4-turbo, GPT-3.5-turbo, Qwen2.5-14B (14 billion parameters), LLaMA3-8B, and OpenBioLLM-8B. Performance was assessed using 98 expert-curated question-answer-context triplets across seven breast cancer categories. Evaluation used both rubric-based scoring (six metrics: fluency, relevance, reliability, consistency, clarity, and clinical impact) and exhaustive pairwise ranking by GPT-4-turbo as judge. Human validation was conducted with 15 practicing oncologists on a 10-query subset.

resultsRAG-enhanced Qwen2.5-14B achieved mean rubric scores of 3.77 versus 3.96 for GPT-4-turbo and pairwise ranking performance of 0.72 versus 0.81 (normalized scale). Although absolute rubric gains were modest (0.02-0.05 on a five-point scale), relative improvements in head-to-head win rates ranged from 16% to 46%. Human expert scores confirmed RAG superiority but were consistently more conservative than LLM judge scores (mean 3.81

conclusionSmall open-source LLMs with optimized RAG can approach state-of-the-art proprietary model performance for clinical decision support. This approach enables scalable, cost-effective, privacy-preserving deployment without recurrent fine-tuning, suggesting potential for real-world clinical implementation on single-graphics processing unit infrastructure under expert supervision.