Integration of AI diagnostic tools into clinical practice for Alzheimer’s disease: barriers and solutions

Abstract

Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide and remains difficult to diagnose in its earliest stages. This narrative review examines developments in artificial intelligence diagnostic tools designed to support clinicians in the detection of Alzheimer's disease. It evaluates systems that analyze brain imaging scans, genetic information, and cognitive assessments, as well as emerging approaches that monitor speech patterns and data from wearable devices. The review identifies six challenges to clinical adoption: limited and unrepresentative data sets; limited transparency of algorithmic decisions; disruption of established clinical workflows; unclear regulatory frameworks; high implementation costs and infrastructure demands; and the potential to widen health disparities. To address these issues, we propose the creation of large collaborative data repositories, the advancement of transparent model interpretation methods, comprehensive clinician education programs, the establishment of clear regulatory pathways, and strategic investment in scalable infrastructure. By confronting these technical, human, and system-level challenges through coordinated efforts, artificial intelligence diagnostic tools can be incorporated into Alzheimer's disease care to enhance early diagnosis and improve patient outcomes across diverse healthcare settings.

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