The potential of artificial intelligence in advancing neuroscience: A systematic review of current applications and models

Abstract

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. 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. 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., SVM, Random Forest) and deep learning approaches (e.g., CNNs, GANs), with several studies employing hybrid models. A comparative analysis of study designs revealed a heavy reliance on public datasets (e.g., 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. 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. • The transformative potential of AI in the advancement of neuroscience is the ability to increase diagnostic accuracy, predict disease progression, and improve imaging techniques. • AI emphasizes the construction of interpretable and personalized models with improvements in models that improve performance and provide insights into individual brain activities. • Recent studies further emphasized the integration of multimodal data, explain ability, and personalized approaches, underscoring AI’s transformative potential in neuroscience.

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