Adaptive cascading AI framework for clinical Alzheimer's assessment

A clinically oriented narrative review proposes an adaptive AI cascading model for Alzheimer’s disease, integrating blood biomarkers and selective imaging escalation to mirror real-world workflows and improve clinical translation.

• The framework uses progressive screening, biomarker-guided assessment, selective imaging escalation, and longitudinal monitoring.
• Enabling methods: sequential decision-making, reinforcement learning, cost-sensitive learning, missing-modality robustness.
• Challenges include data design, validation, integration, and ethical use.

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