Agentic artificial intelligence in cognitive screening: A translational roadmap for dementia care

Area of Science:

• Artificial Intelligence
• Cognitive Science
• Medical Informatics

Background:

• Current dementia screening tools (paper-based, static AI) provide limited, periodic snapshots of cognitive function.
• Cognitive decline is dynamic, influenced by environmental factors, comorbidities, and patient history.
• Existing methods lack adaptivity, feedback integration, and transparent reasoning crucial for clinical trust.

Purpose of the Study:

• To propose agentic AI systems as a novel framework for dynamic and personalized cognitive health assessment.
• To introduce the Cognitive Agent Lab as a practical example of this agentic AI framework.
• To highlight key functional and non-functional aspects of agentic AI for clinical applications.

Main Methods:

• Development of modular agents built upon large language models (LLMs).
• Emphasis on functional aspects: inputs, outputs, and collaborative workflows.
• Focus on non-functional aspects: transparency, adaptivity, robustness, and clinical alignment.

Main Results:

• Agentic AI systems demonstrate potential for collaborative and adaptive cognitive assessment.
• The Cognitive Agent Lab framework provides a model for implementing these systems.
• The proposed approach integrates reasoning and collaboration for enhanced clinical utility.

Conclusions:

• Agentic AI systems represent a significant advancement over static screening tools.
• This framework offers a pathway toward more personalized, explainable, and trustworthy cognitive health solutions.
• The integration of LLMs and modular agents facilitates dynamic and clinically aligned cognitive monitoring.