NeuroAIHub: An AI-Driven Framework for Automated Curation and Discovery of Neuroradiology Data Sets
Year: 2026
Type: article
Abstract: Neuroradiology data sets have significant potential for advancing neuroimaging research, yet identifying relevant and up-to-date resources remains challenging. NeuroAIHub is an artificial intelligence (AI)-driven framework designed to automate data set discovery, improve accessibility, and support structured exploration of neuroradiology data sets. A foundational database was constructed through a multireviewer extraction process with standardized metadata harmonization. To maintain and expand the registry, an AI-based updating workflow performs monthly Web searches, extracts structured metadata from heterogeneous sources using large language models, and submits candidate entries for developer validation before integration. A large language model-powered conversational agent enables natural language data set retrieval, analytical queries, and visualizations, while a structured Web interface supports reproducible filtering. NeuroAIHub currently hosts 180 data sets across 6 diagnostic domains and is available as a Web application (https://neuroai.streamlit.app/) and an open-source Python package (https://github.com/NeuroAIHub-Registry/NeuroAIHub; https://pypi.org/project/neuroaihub). The platform provides a continuously curated resource designed to improve reproducibility, transparency, and efficiency in neuroradiology data set discovery.
DOI: 10.3174/ajnr.a9363
Institutions: Mashhad University of Medical Sciences, Twickenham Plating Group (United Kingdom), Richmond upon Thames College, Tehran University of Medical Sciences, Jahrom University, Jahrom University of Medical Sciences, Shenyang Medical College, Lorestan University of Medical Sciences, Shahid Beheshti University of Medical Sciences, Mayo Clinic, Mayo Clinic in Florida, University of California San Diego, Scripps Clinic Medical Group, Duke University, Duke Medical Center, New York University, University of Pennsylvania
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7155549564