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 (AI) 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 AI technologies and applications in medicine, and then presents a comprehensive review of AI 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 AI technologies in AD analysis. Finally, core research challenges and future research directions are discussed.

The current mode of clinical aided diagnosis of Ocular Myasthenia Gravis (OMG) is time-consuming and laborious, and it lacks quantitative standards. An aided diagnostic system for OMG is proposed to solve this problem. The values calculated by the system include three clinical indicators: eyelid distance, sclera distance, and palpebra superior fatigability test time. For the first two indicators, the semantic segmentation method was used to extract the pathological features of the patient’s eye image and a semantic segmentation model was constructed. The patient eye image was divided into three regions: iris, sclera, and background. The indicators were calculated based on the position of the pixels in the segmentation mask. For the last indicator, a calculation method based on the Eyelid Aspect Ratio (EAR) is proposed; this method can better reflect the change of eyelid distance over time. The system was evaluated based on the collected patient data. The results show that the segmentation model achieves a mean Intersection-Over-Union (mIoU) value of 86.05%. The paired-sample T-test was used to compare the results obtained by the system and doctors, and the $p$ values were all greater than 0.05. Thus, the system can reduce the cost of clinical diagnosis and has high application value.