This research introduces a specialized framework that enhances multimodal large language models (MLLMs) to better analyze 3D brain images for neurological disorders.

• Implements adapter-tuning to efficiently fine-tune MLLMs without retraining entire models
• Incorporates both brain MRI scans and clinical text data for comprehensive diagnosis
• Demonstrates superior performance in detecting Alzheimer's disease and other neurological disorders
• Bridges the gap between 2D and 3D medical imaging analysis in AI

This innovation matters because it provides clinicians with more accurate diagnostic tools that understand spatial relationships in brain scans while maintaining contextual medical knowledge, potentially accelerating diagnosis and improving treatment planning for neurological conditions.

Brain-Adapter: Enhancing Neurological Disorder Analysis with Adapter-Tuning Multimodal Large Language Models