Agentic AI for automated hypothesis testing in Alzheimer's disease and related dementias.

BACKGROUND

Alzheimer's 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.

METHODS

We created a modular AI system with agents for data preparation, planning, analysis, critique, and summarization. Using data from the National Alzheimer's 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.

RESULTS

The 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.

CONCLUSIONS

Our system illustrates a powerful paradigm in which AI can facilitate automated data analysis, expediting advancements in population-level dementia investigations.