Abstract
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.
Highlights
Alzheimer’s disease (AD) remains the most common neurodegenerative disorder worldwide, with a prevalence of 3.9% for individuals over the age of 60 [1,2]
The terms artificial intelligence (AI) and machine learning (ML) [12] refer to the method where a computer can simulate the human processes of learning and reasoning, by analyzing information and performing tasks following a logical sequence
They have disadvantages, mainly linked to inadvertent errors and the inclusion of biases. Tackling these limitations is dependent on Additional to the information presented in the introduction, the advantages of applying algorithms in dementia rely on the capacity of handling data more efficiently and the ability to automate, especially in the development of tools that can aid decision making in the dementia clinics
Summary
Alzheimer’s disease (AD) remains the most common neurodegenerative disorder worldwide, with a prevalence of 3.9% for individuals over the age of 60 [1,2]. Concerning the possibility of improving diagnosis and predicting progression and conversion to AD, it could be beneficial to apply technological approaches such as algorithms, known as artificial intelligence (AI) or machine learning (ML), that are low-cost tools with great performance metrics. In such algorithms, a specific set of features or variables is established, through which a given set of instructions is commanded to discover patterns that can express categorization or associations. This review represents an overview of the main characteristics, current applicability, and future perspectives of the implementation of AI in healthcare, in dementia
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