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Artificial intelligence techniques applied to anxiety disorders recognition: a systematic review.

Edgar Degante-Aguilar¹, Roberto Angel Melendez-Armenta¹, Giovanni Luna-Chontal¹

¹Affective Computing and Educational Innovation Laboratory, Division of Graduate Studies and Research, Tecnológico Nacional de México-Instituto Tecnológico Superior de Misantla, Misantla, Veracruz, Mexico.

Frontiers in Digital Health

|December 10, 2025

Summary

Artificial Intelligence (AI) methods show higher accuracy in detecting anxiety disorders than traditional tests. This review analyzes AI

Keywords:

PICO PRISMA-statement anxiety disorders artificial intelligence mental health

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Area of Science:

Mental Health Diagnostics
Artificial Intelligence in Healthcare
Computational Psychiatry

Background:

Anxiety disorders represent a significant global health burden.
Traditional diagnostic methods for anxiety disorders have limitations in accuracy and accessibility.
The integration of Artificial Intelligence (AI) offers potential for improved diagnostic tools.

Purpose of the Study:

To systematically review and analyze current Artificial Intelligence (AI) methods for anxiety disorder detection.
To evaluate the accuracy and scope of AI-driven diagnostic approaches.
To provide insights for researchers, clinicians, and developers in AI-based mental health.

Main Methods:

Systematic review conducted following PRISMA guidelines.
Searches performed across IEEE Xplore, PubMed, ScienceDirect, and SpringerLink.
Included studies focused on quantitative data and AI techniques for anxiety disorder diagnosis.

Main Results:

Analysis of 119 studies on Machine Learning and Deep Learning applications.
AI methods utilized diverse data sources: self-reports, physiological, and social network data.
AI-driven detection demonstrated superior accuracy compared to traditional methods.

Conclusions:

Artificial Intelligence plays a crucial role in enhancing anxiety disorder detection and treatment.
This review offers a comprehensive overview of AI advancements in anxiety diagnostics.
Findings guide future research and integration of AI in mental healthcare practices.