Application of Artificial Intelligence in the Diagnosis of Neurodegenerative Diseases: The Case of Alzheimer's Disease

Chengyu Wang; Zihe Zhong

doi:10.62051/5y342m14

Authors

Chengyu Wang
Zihe Zhong

DOI:

https://doi.org/10.62051/5y342m14

Keywords:

Artificial intelligence; Diagnosing Alzheimer’s disease; Diagnostic methods.

Abstract

In the arena of Alzheimer's Disease (AD) research, the utilization of Artificial Intelligence (AI) for the early diagnosis and prediction of the disease is identified as a crucial avenue of exploration. The impetus for this line of inquiry is rooted in the urgent need for treatments and strategies that can slow the progression of AD, particularly through early detection and prognostication. The application of AI, in conjunction with big data, facilitates the integration of diverse datasets related to AD and actual clinical cases. This approach is instrumental in identifying genes associated with AD, thereby uncovering the disease's biological underpinnings and potentially leading to the development of novel therapeutic modalities. Such advancements promise not only to reduce healthcare costs but also to alleviate the emotional burden on families affected by this condition. The primary research methodology of this paper is an extensive review of existing literature, focusing on the evolution, challenges, and future prospects of AI in the early diagnosis and prediction of AD. Research to date has demonstrated that various teams have proposed a multitude of models, each with its unique strengths, showcasing significant progress in the application of AI within this field. The findings reveal that while AI's application in the early diagnosis and prediction of AD has made commendable strides, the models employed still offer considerable room for improvement. Moreover, several challenges remain unresolved. Hence, the current body of research not only highlights AI's contributions to AD research but also underscores the necessity for further investigation and refinement in this vital area. In conclusion, this review emphasizes the developments achieved in the application of AI for the diagnosis and prediction of AD, while also identifying areas for potential enhancement. The significance of this study lies in its detailed depiction of the current landscape of AI in AD research, accentuating the importance of continued exploration and optimization in combating this formidable disease. Ultimately, this research seeks to forge more effective strategies against AD, underscoring the potential of AI as a powerful tool in understanding and addressing this debilitating condition.

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