# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders  
1 Department of Automatic Control, Computers, and Electronics, Faculty of Mechanical and Electrical Engineering, Petroleum-Gas University of Ploiesti, 39 Bucharest Avenue, 100680 Ploiesti, Romania; [EMAIL_ADDRESS]  
1 Department of Automatic Control, Computers, and Electronics, Faculty of Mechanical and Electrical Engineering, Petroleum-Gas University of Ploiesti, 39 Bucharest Avenue, 100680 Ploiesti, Romania; cosmina.rosca@uppg-ploiesti. example, nonspecific symptoms or difficulties in early identification are areas of interest in the context of AI applied to the neurological field. Thus, works from the specialized literature that address neurological diseases related to AI tools will be identified. The tools in this field can accelerate the diagnostic process, directly impacting the patient. Nowadays, AI tools are used in numerous domains [1; 2; 3; 4] including medicine to help reduce human errors and personalize treatments. Other benefits introduced by AI tools include the increased volume of medical data requiring advanced computational solutions specific to imaging, genomics, and the clinical sector [5; 6; 7; 8]. This paper reviews machine learning (ML) applications in neurological diseases, especially neurodegenerative diseases. This study addresses the following research questions (RQ):  
* RQ1: What ML methods are used to diagnose neurodegenerative diseases? * RQ2: What are the performances achieved by using ML methods compared to traditional ones in diagnosing neurodegenerative diseases? * RQ3: What are the main challenges of ML algorithms used in the field of neurological diseases (Neuro-ML)? * RQ4: What are the main limitations of Neuro-ML? The Web of Science (WOS) database was used to identify the research stage in the field. The research encompasses both general searches and targeted searches for specific diseases. The selection criteria for the articles include studies published between 1 January 2020 and 27 March 2025 that employ ML for diagnosis, prediction, classification, and prognosis, corresponding to the 5 years analyzed. The research includes 10 analyses to evaluate the applicability of Neuro-ML. These cover aspects such as keyword distribution, which highlights research priorities, and country analysis, which underscores global leaders in Neuro-ML. Year and editor analyses reveal the level of interest and the role of platforms in disseminating results. Comparing ML algorithms using WOS and a customized method proposed by the authors offers complementary perspectives on the popularity of ML models. Identifying underrepresented ML algorithms highlights gaps in the literature concerning Neuro-ML. The analysis of disease distribution outlines the concentration of research on certain conditions. Additionally, the research also includes peer-reviewed articles from previous studies. These analyses identify gaps in the literature and guide future research directions. The methodology used represents a bibliometric and meta-analytic analysis of the existing literature. The authors defined four research questions and conducted 10 analyses, including keyword analysis, analysis of publications' geographical and temporal distribution, identification of ML algorithms used in neuroscience, and analysis of research gaps:  
* For RQ1, the authors extensively analyzed the specialized literature using WOS. The search results focused on the degree of use of ML algorithms in diagnosing neurological diseases. Thus, the ML models were classified based on their frequency of use and the performance reported in various studies. Subsequently, the distributions of these algorithms were compared based on customized procedures and data provided by WOS. * For RQ2, the methodology aimed at evaluating the performance of the ML algorithms. Therefore, the authors analyzed accuracy, sensitivity, F1-Score, and area under the curve (AUC). These metrics were compared with the results reported in traditional studies.