Abstract Summary
In high-stakes domains such as health and biology, information retrieval systems must ensure accuracy while also supporting equitable access and protecting sensitive data. However, many state-of-the-art biomedical IR solutions rely on proprietary cloud infrastructures, raising concerns over cost, reproducibility, and patient privacy. We present a fully open-source retrieval-augmented question answering framework that accurately manages QA against the entire PubMed collection (over 38M documents) using modest, local, consumer-grade hardware. Inspired by BioASQ, our system combines sparse and dense retrieval with a lightweight local LLM for evidence-grounded biomedical QA. Experiments show that strong retrieval quality and real-time performance are achievable without reliance on commercial APIs or large GPU clusters. By reducing infrastructure barriers around on-premises data, this work provides a concrete path toward democratizing trustworthy biomedical IR for hospitals, universities, and healthcare organizations worldwide.
