Unravelling Data Challenges in AI-Driven Alzheimer's Research

Abstract

Alzheimer's disease (AD) is a rapidly developing public fitness subject, affecting thousands and thousands of human beings globally and placing a sizable strain on healthcare systems. With the upward push of synthetic intelligence (AI) technologies, there was a renewed interest in using records-driven approaches to apprehend and potentially deal with advert. In this chapter, the authors aim to get to the bottom of these data challenges in AI-pushed Alzheimer's studies, exploring ability solutions and destiny instructions. They first speak about the various forms of data used in AD studies. They then examine the common facts best troubles and biases that can have an effect on AI fashions, and recommend processes to mitigate those demanding situations. In the end, they speak of the capability of collaborative statistics-sharing projects to conquer data challenges and advance AI-driven Alzheimer's studies. Through information and addressing these information challenges, the authors can pave the way for greater correct and impactful AI-driven solutions in the fight against this devastating disease.

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