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

Abstract

Neurological diseases are increasingly diverse and prevalent, presenting significant challenges for their 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 1 January 2018 and 31 December 2024. Key journals, authors, and research collaborations were identified, highlighting major contributions to the field. Science mapping investigated areas of research focus, such as biomechanical data and gait analysis including AI methodologies for neurological disease diagnosis. Co-occurrence analysis of author keywords allowed for 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 but relatively limited collaborative interest in this domain, with only a few highly cited authors, documents, and journals driving the research. Meanwhile, the literature review highlights the current methodologies and advancements in this field. This study offers a foundation for future research and provides researchers, clinicians, and occupational therapists with an in-depth understanding of AI’s potentially transformative role in neurology.