Experimental Setup & Results of AI-Driven Multimodal Alzheimer’s Detection Framework

The complexity, multimodality and longitudinal nature of disease progression continues to be a problem with regard to early diagnosis of Alzheimer disease (AD). Traditional machine learning models that rely on correlation are prone to a temporal leakage, site-specific bias and low causal validity, which lowers their clinical reliability. This article represents the experiment and findings of an AI-based multimodal Alzheimer detection system combining causal inference, temporal graph verification, uncertainty-sensitive multisite transport, sequential clinical decision modeling, and multimodal digital-twin simulation. The framework adds MRI morphometry, PET SUVRs, plasma, CSF biomarkers, genetics, EEG, speech features, neuropsychological scores, and demographics to a causally constrained and temporally faithful learning structure. Experimental testing is done on large scale longitudinal cohorts such as ADNI, AIBL, and OASIS-3 with stringent rolling-origin validation procedures so that future information leakage is absent. The outcomes indicate high counterfactual fidelity, great temporal generalization, multisite calibrated robustness, and increased diagnostic efficiency. The proposed system can attain 12-month AUROC of up to 0.88, site-wise conformal coverage of more than 90% and beat diagnostic cost and latency by ensuring optimized decision pathways. These conclusions show that causality, temporal integrity, and awareness of uncertainties in multimodal AI systems can significantly enhance the quality and clinical usability of early Alzheimer disease detection.