Precision Psychiatry: Leveraging Multi-Omics and AI for Personalized Mental Health Treatment

Abstract

One of the main causes of the global disease burden is psychiatric disorders. However, due to the complex relationships between genetic, epigenetic, environmental, and neurological factors, diagnosing and treating these conditions remains challenging. Precision psychiatry, driven by multi-omics and artificial intelligence (AI), offers a novel approach to understanding mental health conditions and developing personalized treatments. This review examines the contributions of genome integration, transcriptomics, epigenomics, proteomics, metabolomics, and microbiome studies to psychiatric research and diagnosis. The area has changed as a result of recent developments in AI, particularly in machine learning and deep learning, combined with information from patient-reported outcomes, electronic health records, and neuroimaging. AI models facilitate the creation of customized treatment regimens, enhance pharmacogenomic predictions, and aid in the identification of biomarkers. Furthermore, the interpretability issues of these models are addressed by the emergence of explainable AI (XAI), which facilitates more transparent healthcare choices. The broad clinical application of precision psychiatry is, however, hindered by several challenges, including the difficulty in integrating multi-omics data, ethical concerns about the use of AI in mental health, and the need for thorough validation across diverse populations. Computational biologists, neuroscientists, psychiatrists, and clinicians from other disciplines must collaborate to address these challenges and develop scalable, reliable, and ethically sound frameworks for precision medicine in psychiatry. This study lays the groundwork for future research and clinical practice by highlighting the potential for integrating AI and multi-omics technology to revolutionize psychiatric care. Precision psychiatry can transition from a trial-and-error method to a tailored and predictive one by utilizing sophisticated computational tools, ultimately improving patient outcomes and mental health treatment. This study is among the first comprehensive efforts to explore the integration of multi-omics and AI, especially XAI, within the context of precision psychiatry, establishing a translational model for tailored mental health treatment.

Received: 25 April 2025 | Revised: 5 August 2025 | Accepted: 17 October 2025

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
The data supporting the findings of this study are available upon request from the corresponding author.

Author Contribution Statement
Oluwafikayo Seun Adeyemi-Benson: Conceptualization, Methodology, Software, Validation, Writing – original draft, Writing – review & editing.