<p>Artificial intelligence (AI) has achieved remarkable success in the diagnosis of Alzheimer’s disease (AD) in the literature, where many of the models use multi-modal methods including neuroimaging, cerebrospinal fluid, genetics, and cognitive assessment. But clinical adoption of these systems is still limited since most systems are developed in an idealized setting, as cost-effective and specialized diagnostic studies are not universally accessible. We discuss the translation of benchmark performance of AI to real-world dementia care pathways. A practical framework that would be useful for scalable, equitable, and clinically deployable AI-assisted dementia care. In fact, recent advancements in blood-based biomarkers such as plasma phosphorylated tau, glial fibrillary acidic protein, and neurofilament light chain are providing new opportunities for a flexible and minimally invasive diagnosis method. Based on these advances, we propose a clinically grounded AI-assisted cascading model which mirrors real-world workflows via progressive screening, biomarker-guided assessment, selective imaging escalation, and longitudinal prognostic monitoring. We further discuss enabling methods such as sequential decision-making, reinforcement learning, cost-sensitive learning, missing-modality robustness, and explainable AI. Finally, we outline the challenges for data design, for future validation and integration into healthcare systems, and ethical use.</p>