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Alzheimer's disease (AD) is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function, but the etiology of AD is still unclear. With the explosive growth of electronic health data, the application of artificial intelligence (Al) in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches, and improving the disease's diagnostic and prognostic outcome. This paper first briefly introduces Al technologies and applications in medicine, and then presents a comprehensive review of Al in AD. In simple, it includes etiology discovery based on genetic data, computer-aided diagnosis (CAD), computer-aided prognosis (CAP) of AD using multi-modality data (genetic, neuroimaging and linguistic data), and pharmacological or non-pharmacological approaches for treating AD. Later, some popular publicly available AD datasets are introduced, which are important for advancing Al technologies in AD analysis. Finally, core research challenges and future research directions are discussed.
Zhao et al. (Mon,) studied this question.