Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review

Abstract

In the context of advancing healthcare, the diagnosis and treatment of cognitive disorders, particularly Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD), pose significant challenges. This review explores Artificial Intelligence (AI) and Machine Learning (ML) in neuropsychological assessment for the early detection and personalized treatment of MCI and AD. The review includes 37 articles that demonstrate that AI could be an useful instrument for optimizing diagnostic procedures, predicting cognitive decline, and outperforming traditional tests. Three main categories of applications are identified: (1) combining neuropsychological assessment with clinical data, (2) optimizing existing test batteries using ML techniques, and (3) employing virtual reality and games to overcome the limitations of traditional tests. Despite advancements, the review highlights a gap in developing tools that simplify the clinician’s workflow and underscores the need for explainable AI in healthcare decision making. Future studies should bridge the gap between technical performance measures and practical clinical utility to yield accurate results and facilitate clinicians’ roles. The successful integration of AI/ML in predicting dementia onset could reduce global healthcare costs and benefit aging societies.

Is Virtual Reality Orientation Therapy Useful to Optimize Cognitive and Behavioral Functioning Following Severe Acquired Brain Injury? An Exploratory Study

“…There are two main types of cognitive rehabilitation methods: traditional (paper-and-pencil exercises) and computer assisted (computer-based cognitive rehabilitation. Both methods use cognitive strategies to help patients improve their attention and concentration, visual processing, language, memory, reasoning, problem-solving, and executive functions [24,25].…”

Neuropsychological Assessment for Early Detection and Diagnosis of Dementia: Current Knowledge and New Insights

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A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

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