Integrating Multimodal AI Techniques and MRI Preprocessing for Enhanced Diagnosis of Alzheimer’s Disease: Clinical Applications and Research Horizons

A progressive neurological disorder, Alzheimer’s disease has a significant social and economic impact and has become more problematic for world health. Early and precise diagnosis are critical to enable timely intervention to improve patient outcomes. This study introduces a new multimodal AI framework integrating advanced MRI preprocessing techniques and speech analysis for the enhancement of the detection of AD. A strong preprocessing pipeline including noise reduction, normalization, skull stripping, and segmentation is utilized to prepare the MRI data. Cognitive and acoustic biomarkers are extracted through spectrograms and pre-trained linguistic models to create speech features. The architecture involves the application of Vision Transformers for spatial analysis and a hybrid CNN-RNN architecture to derive contextual insights, making the framework applicable to multimodal fusion for holistic diagnosis. The framework achieved an accuracy of 94.2% with precision, recall, and F1-scores higher than 92% in the experimental evaluation on a diverse dataset. Comparative analysis with several recent studies further emphasizes that the framework provides better diagnostic performance on key metrics. This work advances early AD detection by incorporating complementary data modalities, addressing existing gaps in unimodal approaches, and providing a scalable, interpretable solution for clinical application.