Mild Cognitive Impairment (MCI) detection from multimodal data represents a cutting-edge approach to early diagnosis by integrating various data types, such as neuroimaging, genetic information, cognitive test scores, and even speech or behavioral data. This method leverages machine learning algorithms to analyze patterns and correlations across these diverse data sources, providing a more comprehensive assessment of cognitive decline. By combining insights from different modalities, the model can capture subtle changes in brain structure, cognitive function, and behavior that might be missed when relying on a single data type. This multimodal approach enhances the sensitivity and specificity of MCI detection, potentially enabling earlier and more accurate interventions for individuals at risk of progressing to Alzheimer’s disease.