Emerging trends in the evolution of neuropsychology and artificial intelligence: A comprehensive analysis

Abstract

Neuropsychological evaluations are valuable in neurosurgery because they comprehensively evaluate cognitive, affective, and behavioral functioning to optimize patient outcomes. Incorporating artificial intelligence (AI) into neuropsychology offers optimistic advances, with machine learning models assisting in classifying behavioral, cognitive, and functional impairments while minimizing the number of tests. AI-based approaches have demonstrated accurate classification outcomes, providing potential alternatives to time-consuming and non-ecological conventional evaluations. This research uses data from the Scopus database to examine the trends of neuropsychological-based AI in cognitive neuroscience, mental health, and neurodegenerative disorders. The study emphasized the potential of artificial intelligence in neuropsychology research and identified several research themes. The analysis of bibliometrics may efficiently assess the developments and impact of neuropsychology research, providing insights into academic output and predicting future trends. Future research should consider utilizing alternative databases, employing a multisource strategy, incorporating additional keywords, and building upon the foundational knowledge provided by this study. Despite its limitations, this study provides significant insights and paves the way for future neuropsychology-based artificial intelligence research. Furthermore, investigating significant topics and key issues in the neuropsychology and artificial intelligence debate adds new perspectives to the corpus of literature. This analysis can help identify gaps, controversies, and areas of future exploration within the field. The study also highlights the importance of learning and intelligent computation in neuropsychology, providing a conceptual methodology based on a comprehensive review of the most recent research.