The Potential of Artificial Intelligence in Advancing Neuroscience: A Systematic Review of Current Applications and Models Publisher



Afroughi F ; Seyedalinaghi S ; Mirzapour P ; Shirdel S ; Parmoon Z ; Khorshidi MM ; Mansouri S ; Sheykhi M ; Popoola Y ; Mehraeen E
Source: Intelligence-Based Medicine Published:2026

Abstract

Introduction Artificial intelligence (AI) is the simulation of human intelligence, in which machines perform problem-solving like the human brain. AI and neuroscience are interrelated. In this study, a systematic review of current AI models and applications was conducted to consider the potential of AI in advancing neuroscience. Methods Relevant articles were selected based on a search in three reputable databases, including Web of Science, PubMed, and Scopus. Two independent researchers conducted the selection process in two stages. Results A total of 99 studies (2019–2024) met PRISMA criteria. Of these, 83 studies focused on specific brain disorders—most notably Alzheimer's disease (n = 26), stroke (n = 14), epilepsy (n = 7), and Parkinson's disease (n = 7)—while 22 addressed broader neuroscience applications. A range of AI methods were applied, including traditional machine learning techniques (e.g., Support Vector Machines (SVM), Random Forest) and deep learning approaches (e.g., Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs)), with several studies employing hybrid models. A comparative analysis of study designs revealed a heavy reliance on public datasets (e.g., Alzheimers Disease Neuroimaging Initiative (ADNI)) for Alzheimer's research, while studies on other disorders predominantly utilized private cohorts. Regarding validation, the majority of studies employed internal cross-validation strategies, with fewer utilizing independent external datasets to test generalizability. Conclusion The transformative potential of AI in advancing neuroscience lies in its ability to increase diagnostic accuracy, predict disease progression, and enhance imaging techniques. Future research should focus on refining AI methods to enhance generalizability and foster collaborations between AI practitioners and neuroscientists. © 2025 The Authors.