SynthesisFrontiers in digital health2025
Artificial intelligence techniques applied to anxiety disorders recognition: a systematic review.
Synthesis in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Evolutionary trajectory and knowledge structure of wearable devices for health management: A bibliometric analysis (1997-2025).Journal of family medicine and primary care · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251026205, identifier CRD420251026205.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.