認知スクリーニングにおけるエージェンティック人工知能：認知症ケアのためのトランスレーショナルロードマップ

Conventional dementia screening tools, whether paper tests or static artificial intelligence (AI) models, capture only a snapshot of cognition and need to be adminstered periodically. Yet decline is dynamic, shaped by environment, comorbidities, and life history. Current approaches rarely adapt, integrate feedback, or provide transparent reasoning that clinicians can trust. We propose agentic AI systems: modular agents built on large language models (LLMs) that collaborate, adapt, and mirror interdisciplinary care. The Cognitive Agent Lab exemplifies this framework, emphasizing functional aspects (inputs, outputs, workflows) and non-functional aspects (transparency, adaptivity, robustness, clinical alignment). Embedding reasoning and collaboration, agentic AI offers a roadmap toward more personalized and explainable cognitive health tools.