Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical Data: A Scopus-Based Bibliometric Analysis

Résumé fourni par la source

Neurological diseases are increasingly diverse and prevalent, presenting significant challenges for timely and accurate diagnosis. The aim of the present study is to conduct a bibliometric analysis and literature review in the field of neurology to explore advancements in the application of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL). Using VOSviewer software (version 1.6.20.0) and documents retrieved from the Scopus database, the analysis included 113 articles published between January 1, 2018, and December 31, 2024. Key journals, authors, and research collaborations were identified, highlighting major contributions to the field. Science mapping revealed research focus areas such as, biomechanical data, gait analysis, and AI methodologies for neurological disease diagnosis. The co-occurrence analysis of author keywords allowed the identification of four major themes: (a) machine learning and gait analysis; (b) sensors and wearable health technologies; (c) cognitive disorders; and (d) neurological disorders and motion recognition technologies. The bibliometric insights demonstrate a growing and collaborative interest in this domain, while the literature review highlights current methodologies and advancements. This study offers a foundation for future research and provides researchers, clinicians, and occupational therapists with an in-depth understanding of AI's transformative role in neurology.

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