• Application of Artificial Intelligence Algorithms in Interpreting Psychiatric Biomarkers: A Comprehensive Review
  • Samin Hamidi,1,* Ali Abolhassani,2
    1. Research Center of Psychiatry and Behavioral Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
    2. Department of Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran


  • Introduction: The convergence of artificial intelligence (AI) and psychiatric biomarker research represents a transformative approach to mental health diagnostics and treatment. While traditional psychiatric diagnosis relies primarily on clinical observation and symptom assessment, AI algorithms demonstrate unprecedented capability in detecting complex, multidimensional patterns within biological datasets that may not be apparent through conventional analytical approaches.
  • Methods: This comprehensive review examines the current landscape of AI-driven biomarker interpretation in psychiatry, where machine learning algorithms—including support vector machines, deep learning networks, and ensemble methods—are being applied to diverse biological data types encompassing neuroimaging, genomics, proteomics, and metabolomics. This review systematically evaluates the application of AI methodologies across major psychiatric disorders, including schizophrenia, major depressive disorder, bipolar disorder, and anxiety disorders, synthesizing findings from neuroimaging studies, genetic association analyses, and peripheral biomarker investigations.
  • Results: Our analysis reveals that while AI algorithms show remarkable promise in identifying novel biomarker signatures and improving diagnostic accuracy, significant challenges remain in terms of reproducibility, generalizability across diverse populations, and integration into existing clinical workflows.
  • Conclusion: Additionally, this review addresses ethical considerations, data privacy concerns, and the need for standardized protocols in AI-driven psychiatric research.
  • Keywords: Artificial Intelligence in Psychiatry, Psychiatric Biomarkers, Machine Learning Algorithms