Neuroradiology datasets hold 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 dataset discovery, improve accessibility, and support structured exploration of neuroradiology datasets. A foundational database was constructed through a multi-reviewer 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 (LLMs), and submits candidate entries for developer validation before integration. An LLM-powered conversational agent enables natural-language dataset retrieval, analytical queries, and visualizations, while a structured web interface supports reproducible filtering. NeuroAIHub currently hosts 180 datasets across six diagnostic domains and is available as a web application (https://neuroai.streamlit.app/) and 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 dataset discovery.
Gheiji et al. (Fri,) studied this question.