Abstract
BACKGROUND: Artificial intelligence (AI) has become significantly integrated into healthcare, particularly in the diagnosing of neurological disorders. This advancement has enabled neurologists and physicians to diagnose conditions more quickly and effectively, ultimately benefiting patients. AIM: To explore the current status and key highlights of AI-related articles in diagnosing of neurological disorders. METHODS: A systematic literature review was conducted in the Web of Science Core Collection database using the following strategy: TS = ("Artificial Intelligence" OR "Computational Intelligence" OR "Machine Learning" OR "AI") AND TS = ("Neurological disorders" OR "CNS disorder" AND "diagnosis"). The search was limited to articles and reviews. Microsoft Excel 2019 and VOSviewer were utilized to identify major contributors, including authors, institutions, countries, and journals. Additionally, VOSviewer was employed to analyze and visualize current trends and hot topics through network visualization maps. RESULTS: A total of 276 publications from 2000 to 2024 were retrieved. The United States, India, and China emerged as the top contributors in this field. Major institutions included Johns Hopkins University, King's College London, and Harvard Medical School. The most prolific author was U. Rajendra Acharya from the University of Southern Queensland (Australia). Among journals, IEEE Access, Scientific Reports, and Sensors were the most productive, while Frontiers in Neuroscience led in total citations. Central topics in AI-related articles on neurological disorders diagnosis included Alzheimer's disease, Parkinson's disease, dementia, epilepsy, autism, attention deficit hyperactivity disorder, and their intersections with deep learning and AI. CONCLUSION: Research on AI's role in diagnosing neurological disorders is becoming widely recognized for its growing importance. AI shows promise in diagnosing various neurological disorders, yet requires further improvement and extensive future research.