Integration of AI and Neuroscience: New Methods in Studying the Causes of Mental Disorders

Abstract

ObjectiveThis review sought to systematize knowledge about the use of artificial intelligence in neurobiological research of mental disorders and assess its potential in identifying their causes.MethodsA qualitative synthesis of scientific literature from the Scopus and Web of Science databases for 2020-2024 was conducted. A total of 50 sources were selected, including papers describing the use of artificial intelligence in the analysis of neuroimaging, biomarkers, cognitive impairment, and genetic data. A thematic encoding was used to analyse methods, accuracy, and limitations.ResultsMachine learning algorithms have accelerated the processing of large amounts of data, including magnetic resonance imaging, electroencephalogram, and genomic profiles, which has revealed new biomarkers and neural patterns associated with depression and schizophrenia. However, artificial intelligence technologies face several limitations: low specificity, high computational complexity, and problems with reproducibility of results.ConclusionsThe integration of artificial intelligence with neuroscience has significantly advanced the understanding of the aetiology of mental disorders, revealing the complex relationships between genetic, neural, and behavioural factors. The practical significance of the research lies in the potential of artificial intelligence to create personalised approaches to the diagnosis and treatment of mental disorders. This can improve the quality of life of patients and reduce the burden on healthcare systems.

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