EDITORIAL article
Front. Psychiatry
Sec. Computational Psychiatry
Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1588031
This article is part of the Research TopicMachine Learning and Statistical Models: Unraveling Patterns and Enhancing Understanding of Mental DisordersView all 8 articles
Editorial: Machine Learning and Statistical Models: Unraveling Patterns and Enhancing Understanding of Mental Disorders
Provisionally accepted
- Xian Institute of Optics and Precision Mechanics, Chinese Academy of Sciences (CAS), Xi'an, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Collectively, these studies exemplify the transformative potential of machine learning and statistical models in psychiatry. They highlight the importance of leveraging diverse data sources, from neuroimaging and genomics to natural language and behavioral data, to uncover the complex patterns underlying mental disorders. Furthermore, they emphasize the need for explainable and interpretable AI models that can bridge the gap between data-driven insights and clinical practice. As we continue to navigate the challenges of mental health in the modern era, the integration of these advanced analytical techniques holds promise for advancing our understanding, improving diagnostic accuracy, and ultimately enhancing patient outcomes.
Keywords: machine learning (ML), Artificail intelligence (AI), Generative Model, mental disoders, Explainability
Received: 05 Mar 2025; Accepted: 24 Apr 2025.
Copyright: © 2025 Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Quan Wang, Xian Institute of Optics and Precision Mechanics, Chinese Academy of Sciences (CAS), Xi'an, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.