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<p>The integration of smartphones, wearable devices, and artificial intelligence (AI) has revolutionized mental health diagnostics, particularly for depression and anxiety, by enabling real-time data collection and early intervention. This review synthesizes the findings from recent studies on the use of these technologies for diagnostic precision and predictive modeling. Following the for Systematic Reviews and Preferred Reporting Items Meta-Analyses guidelines, a systematic search of PubMed, Scopus, and Web of Science was conducted for publications up to April 2025, resulting in the inclusion of 62 relevant studies. Our critical analysis revealed that, while artificial intelligence demonstrates high accuracy in detecting mental health symptoms, its performance is highly context-dependent. We examined significant challenges, including the lack of generalizability owing to disparate datasets, the critical yet often unstandardized role of feature engineering, and the “black box” nature of complex algorithms that hinder clinical trust. Addressing these limitations requires interdisciplinary collaboration, robust ethical and regulatory frameworks (e.g., GDPR and HIPAA), and scalable interpretable solutions. Future research must prioritize long-term validation, inclusivity across diverse populations, and development of explainable AI to bridge the gap between technological potential and clinical reality.</p>

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Advancing mental health diagnostics: a review on the role of smartphones, wearable devices, and artificial intelligence in depression and anxiety detection

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Huy Hoang,
  • Huong Ha,
  • Hiep Nguyen,
  • Paul Watton,
  • Lua Ngo

摘要

The integration of smartphones, wearable devices, and artificial intelligence (AI) has revolutionized mental health diagnostics, particularly for depression and anxiety, by enabling real-time data collection and early intervention. This review synthesizes the findings from recent studies on the use of these technologies for diagnostic precision and predictive modeling. Following the for Systematic Reviews and Preferred Reporting Items Meta-Analyses guidelines, a systematic search of PubMed, Scopus, and Web of Science was conducted for publications up to April 2025, resulting in the inclusion of 62 relevant studies. Our critical analysis revealed that, while artificial intelligence demonstrates high accuracy in detecting mental health symptoms, its performance is highly context-dependent. We examined significant challenges, including the lack of generalizability owing to disparate datasets, the critical yet often unstandardized role of feature engineering, and the “black box” nature of complex algorithms that hinder clinical trust. Addressing these limitations requires interdisciplinary collaboration, robust ethical and regulatory frameworks (e.g., GDPR and HIPAA), and scalable interpretable solutions. Future research must prioritize long-term validation, inclusivity across diverse populations, and development of explainable AI to bridge the gap between technological potential and clinical reality.