Improving access to rare-disease knowledge: A retrieval-augmented question answering framework for Wilson's disease.
TL;DR
This work introduces WilsonLitQA, a literature-grounded retrieval-augmented question answering resource that enables the scientific community to query the complete PubMed literature on Wilson's disease, capturing canonical biological, clinical, and pathophysiological knowledge related to the disease.
Abstract
Objective
Wilson's disease is a rare genetic disorder with a large but highly fragmented body of biomedical literature spanning more than a century of research. Although thousands of articles are available through PubMed, this knowledge remains difficult to access in a consolidated, query-driven manner for researchers, clinicians, and students. Existing large language models (LLMs) offer conversational access to information but often lack domain grounding, leading to hallucinations and unreliable responses in biomedical settings. In this work, we introduce WilsonLitQA, a literature-grounded retrieval-augmented question answering resource that enables the scientific community to query the complete PubMed literature on Wilson's disease, capturing canonical biological, clinical, and pathophysiological knowledge related to the disease.
Methods
Our framework integrates hybrid information retrieval with generative language models, enabling users to query the Wilson's disease literature as a unified knowledge resource rather than as isolated papers. We have systematically evaluated multiple open-source and proprietary LLMs under zero-shot and few-shot in-context learning settings, assessing their ability to deliver accurate, complete, and non-hallucinated responses when supported by retrieval.
Results
Our results demonstrate that retrieval grounding substantially improves biological accuracy and clinical relevance, and that smaller, deployable language models (on the order of 7B parameters) can perform competitively when paired with a well-designed retrieval pipeline.
Conclusion
To support transparency, reproducibility, and community-driven development, the complete WilsonLitQA implementation is publicly available (https://dhanjal-lab.iiitd.edu.in/wilsonlitqa.html). Together, this work positions retrieval-augmented question answering as a consolidated, continuously extensible knowledge interface for rare diseases, offering the scientific community a practical tool to navigate, interpret, and query the growing biomedical literature on Wilson's disease.