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.