Artificial Intelligence in Mental Health and Neuroimaging: Diagnostic, Predictive, and Therapeutic Advances

Dear Colleagues,

The integration of artificial intelligence and machine learning into neuroscience and mental health research has led to substantial methodological and clinical advances. These technologies enable the efficient analysis of high-dimensional and complex neuroimaging data, support the early detection and classification of psychiatric disorders and facilitate the development of personalized treatment strategies.

With the increasing availability of large-scale neuroimaging repositories, electronic health records and multimodal clinical datasets, artificial intelligence- and machine learning-based approaches are becoming indispensable tools in modern neuropsychiatric research. They play a critical role in identifying neurobiological biomarkers, modeling disease trajectories and improving diagnostic precision and therapeutic decision-making across a wide range of mental health conditions. As a result, artificial intelligence-driven methodologies are reshaping both research paradigms and clinical practice in psychiatry and neuroscience.

This Special Issue aims to bring together state-of-the-art research on the application of artificial intelligence and machine learning in mental health and neuroimaging. The focus is on innovative approaches that bridge computational methods with clinical neuroscience and psychiatry, particularly those that enhance diagnostic accuracy, enable outcome prediction and inform therapeutic interventions.

By providing a multidisciplinary platform, this Special Issue seeks to advance both scientific understanding and clinical translation of artificial intelligence-driven techniques in neuropsychiatry. Contributions that emphasize methodological rigor, clinical relevance and interpretability are especially encouraged, fostering dialog between clinicians, neuroscientists and data scientists.

Topics of interest include, but are not limited to:

• Artificial intelligence-enhanced neuroimaging techniques for psychiatric diagnosis and disease characterization
• Machine learning models for outcome prediction, prognosis and risk stratification in mental health disorders
• Computational methods for biomarker discovery and personalized treatment planning
• Explainable and interpretable artificial intelligence models in neuropsychiatric research and clinical practice
• Multimodal data integration, including neuroimaging, genomics, clinical and electronic health record data
• Artificial intelligence-guided digital therapeutics and clinical decision support systems in mental health care

This Special Issue welcomes original research articles, reviews, narrative reviews, scoping reviews, systematic reviews and meta-analyses. Submissions may address clinical applications, methodological developments, translational research, or interdisciplinary studies involving psychiatry, neuroscience, artificial intelligence, data science and biomedical engineering.

All submitted manuscripts should demonstrate methodological robustness, innovation and clear clinical or translational relevance to mental health and neuroimaging.

Dr. Rossitsa Paunova
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Medicina is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial intelligence
• machine learning
• mental health
• neuroimaging
• psychiatry
• neuroscience
• biomarkers
• predictive modeling
• explainable artificial intelligence
• digital therapeutics