arXiv:2605.20525v1 Announce Type: cross Abstract: We present NeuroQA, a large-scale benchmark for visual question answering in 3D brain magnetic resonance imaging (MRI), with 56,953 QA pairs from 12,977 subjects across 12 datasets. It spans ages 5-104 and five clinical domains: Alzheimer's, Parkinson's, tumors, white matter disease, and neurodevelopment. Unlike prior medical Visual Question Answering (VQA) efforts that operate on 2D slices or rely on narrow diagnostic labels, NeuroQA pairs every item with a full 3D volume. It evaluates 11 clinically grounded reasoning skills across Yes/No, mul
The development of NeuroQA aligns with the increasing sophistication of AI models and the growing availability of large-scale medical imaging datasets, pushing the boundaries of medical AI applications.
This benchmark represents a significant step towards more accurate and clinically useful AI in medical diagnostics, particularly for complex 3D imaging, which could revolutionize disease detection and patient management.
The shift from 2D slice analysis to full 3D volume processing with clinically grounded reasoning skills for VQA in brain MRI establishes a new standard for medical AI benchmarks and development.
- · AI healthcare startups
- · Medical imaging companies
- · Neurology departments
- · Patients with neurological conditions
- · Traditional diagnostic methods
- · Legacy medical imaging software
Improved early detection and differential diagnosis of neurological diseases through AI.
Accelerated development of personalized treatment plans based on more nuanced diagnostic insights from AI.
Potential for remote, automated neurological assessments, democratizing access to specialized medical expertise globally.
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