Alzheimer’s disease (AD) and its related dementias have signaled a “red-alert” to the aging population worldwide. The multifold increase of AD incidence has cautioned public health advocates and clinicians globally to discover newer screening modalities for risk detection and early prevention approaches for a better quality of life among the elderly. Conventional screening tools for AD detection are limited by their diagnostic performance values due to variants of different classifications and scoring methods. These methods have limited capabilities to detect or predict the risk of AD or dementias among individuals in large communities. With borderless internet connectivity and the rise of big public health data, screening approaches for risk prediction of AD and related dementias have conveyed a new promise through the dawn of artificial intelligence (AI). Using machines through programmable computers and robust statistical approaches, AD risk prediction showed better accuracy, reliability, ability to screen large communities, and cost effectiveness. While the deployment of such intelligent systems shows the path to healthcare revolution, concerns arise on regulatory and ethical issues of data breach and sharing. This chapter systematically appraises the concepts and promising benefits of AI technology within healthcare for AD risk prediction across communities, and its possible concerns to be tackled prior to large-scale implementation.
Handbook of Decision Support Systems for Neurological Disorders, Volume , 1 January 2021,