Agentic AI for automated hypothesis testing in Alzheimer's disease and related dementias
Saichandran, K. S.; Elzokm, K.; Guney, O. B.; Kolachalama, V. B.
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BackgroundAlzheimers disease and related dementias (ADRD) are multifactorial disorders driven by genetic, vascular, inflammatory, lifestyle, and biological factors interacting over decades, requiring population-level analyses. However, utilizing multimodal datasets is labor-intensive, impeding discovery and equity. Multi-agentic AI systems promise workflow automation but lack ADRD-specific adaptations. MethodsWe created a modular AI system with agents for data preparation, planning, analysis, critique, and summarization. Using data from the National Alzheimers Coordinating Center (NACC) (n=48,876), we tested 100 literature-derived ADRD hypotheses, subsampled cohorts (100-10,000 participants), and 100 non-ADRD controls via iterative loops. ResultsThe system verified 42.0{+/-}2.8 hypotheses by rejecting the null, found no evidence for 16.6{+/-}1.1, and deemed 41.4{+/-}2.3 not testable, with consistency in negated versions. Verification rates rose from 10.4 (n=100) to 35.4 (n=10,000); controls showed 85.2{+/-}0.6 non-testable. ConclusionsOur system illustrates a powerful paradigm in which AI can facilitate automated data analysis, expediting advancements in population-level dementia investigations.
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