IA agentica para la prueba automatizada de hipótesis en la enfermedad de Alzheimer y demencias relacionadas

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.

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.

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.

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