Multimodal AI is Advancing Biomarker Discovery in Parkinson’s and Alzheimer’s Disease: Evidence, Limitations, and the Translational Gap

Artificial intelligence is rapidly expanding the biomarker landscape for Parkinson’s and Alzheimer’s diseases. Multimodal systems combining neuroimaging, speech, gait, physiological signals, cognitive measures, and molecular markers show substantial research-stage performance. In studies with extractable accuracy and denominator data, reported Parkinson’s disease classification accuracies ranged from 94.25% (95% Wilson CI: 93.78–94.68%) to 98.6% (95% Wilson CI: 95.71–99.67%), while Alzheimer’s disease diagnostic or conversion-prediction accuracies ranged from 65.8% (95% Wilson CI: 62.54–68.87%) to 99.26% (95% Wilson CI: 97.89–99.75%). Therefore, the field is no longer limited by the absence of candidate AI biomarkers. However, the central bottleneck is translational validation. External validation remains uncommon across medical AI, with rates of 3.7% in high-impact medical informatics studies, 6% in diagnostic medical-imaging AI studies, and 14.7% in ICU AI scoring-system studies. In neurodegenerative disease, many models still rely on single cohorts, internal splits, incomplete metric reporting, and weak linkage between AI-derived digital phenotypes and established molecular or clinical biomarkers. Consequently, performance degradation in real-world populations, reduced specificity, calibration drift, and cohort bias help explain why extensive machine learning activity has not yet translated into routine clinical deployment. In this narrative review, we therefore synthesize quantitative evidence across motor, cognitive, neuropsychiatric, remote-monitoring, and neuroimaging domains. We argue that progress in AI for Parkinson’s and Alzheimer’s disease now depends less on additional model novelty alone than on standardized validation pipelines, external cohort testing, calibration assessment, interpretable multimodal fusion, and clinically meaningful endpoints. Nonetheless, with these safeguards, multimodal AI can move from promising biomarker discovery toward reproducible, clinically deployable precision diagnostics in neurodegenerative disease.