SYSTEMATIC REVIEW article
Front. Digit. Health
Sec. Digital Mental Health
This article is part of the Research TopicEmotional Intelligence AI in Mental HealthView all 11 articles
Artificial Intelligence Techniques Applied to Anxiety Disorders Recognition: A Systematic Review
Provisionally accepted
Edgar Degante-Aguilar
Roberto Angel Melendez-Armenta*
Giovanni Luna-ChontalFrancisco Javier Fernandez-Dominguez- Misantla Higher Technological Institute (ITSM), Misantla, Mexico
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This Systematic Review was prospectively registered in PROSPERO with registration number CRD420251026205. Objective: This Systematic Review aims to provide a comprehensive analysis of the current state of anxiety disorder detection methods using Artificial Intelligence (AI), focusing on their accuracy and the scope of research. This review is tailored for researchers, clinicians, and technology developers seeking to understand the advancements in AI-driven mental health diagnostics. Methodology: A Systematic Review was conducted following the PRISMA Statement guidelines, utilizing databases such as IEEE Xplore, PubMed, ScienceDirect, and SpringerLink. The review included studies focusing on the diagnosis of anxiety disorders using quantitative data and AI techniques, excluding those solely focused on depression or lacking experimental datasets. Results: A total of 119 studies were analyzed, revealing the application of Machine Learning and Deep Learning techniques in detecting anxiety disorders from diverse data sources, including self-reports, physiological data, and social network data. The findings indicate that AI-driven methods demonstrate higher accuracy compared to traditional anxiety disorder detection tests, providing valuable insights for clinicians and researchers exploring improved diagnostic tools. Conclusions: This review highlights the critical role of AI in optimizing the detection and treatment of anxiety disorders. It offers a current and detailed overview of advancements in this field, making it a key resource for researchers, healthcare professionals, and technology developers aiming to integrate AI into mental health practices. The synthesis of findings provides a clear understanding of the current landscape and potential future directions in AI-based anxiety detection.
Keywords: Anxiety Disorders, artificial intelligence, PICO, PRISMA-Statement, mental health
Received: 13 Jun 2025; Accepted: 23 Oct 2025.
Copyright: © 2025 Degante-Aguilar, Melendez-Armenta, Luna-Chontal and Fernandez-Dominguez. 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: Roberto Angel Melendez-Armenta, ra.melendezarmenta@gmail.com
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.