Advances in Artificial Intelligence-Based Depression Diagnosis: A Systematic Review

Liangguo Wang; Jiaqian Wu

doi:10.62762/TETAI.2025.416797

Article Information

Published in ICCK Transactions on Emerging Topics in Artificial Intelligence

Volume/Issue Volume 2, Issue 3, 2025

Pages 148-156

Cited by 2 (Crossref) 2 (Scopus)

Abstract

Depression affects approximately 280 million people worldwide, yet remains substantially underdiagnosed due to the limitations of clinical assessment tools. This systematic review synthesizes 38 studies (2013--2025) on AI-based depression detection across voice, facial expression, physiological signals, and social media modalities, following PRISMA 2020 guidelines. Multimodal fusion consistently outperforms unimodal approaches, with reported F1 gains of 5-15% on benchmark datasets; however, no reviewed system has undergone prospective clinical validation, and reported accuracy figures should be interpreted as upper bounds on clinical utility. Critical open challenges include cross-cultural generalizability, longitudinal monitoring, interpretability, and ethical governance. This review reframes the field's central question from can AI detect depression? to under what conditions and with what governance can it be safely deployed?

Keywords

depression artificial intelligence multimodal features deep learning intelligent detection

Data Availability Statement

Not applicable.

Funding

This work was supported by the Key Project of Natural Science Foundation of Xinjiang Hetian College under Grant 2025ZR002.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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Wang, L., & Wu, J. (2025). Advances in Artificial Intelligence-Based Depression Diagnosis: A Systematic Review. ICCK Transactions on Emerging Topics in Artificial Intelligence, 2(3), 148-156. https://doi.org/10.62762/TETAI.2025.416797

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TY  - JOUR
AU  - Wang, Liangguo
AU  - Wu, Jiaqian
PY  - 2025
DA  - 2025/08/27
TI  - Advances in Artificial Intelligence-Based Depression Diagnosis: A Systematic Review
JO  - ICCK Transactions on Emerging Topics in Artificial Intelligence
T2  - ICCK Transactions on Emerging Topics in Artificial Intelligence
JF  - ICCK Transactions on Emerging Topics in Artificial Intelligence
VL  - 2
IS  - 3
SP  - 148
EP  - 156
DO  - 10.62762/TETAI.2025.416797
UR  - https://www.icck.org/article/abs/TETAI.2025.416797
KW  - depression
KW  - artificial intelligence
KW  - multimodal features
KW  - deep learning
KW  - intelligent detection
AB  - Depression affects approximately 280 million people worldwide, yet remains substantially underdiagnosed due to the limitations of clinical assessment tools. This systematic review synthesizes 38 studies (2013--2025) on AI-based depression detection across voice, facial expression, physiological signals, and social media modalities, following PRISMA 2020 guidelines. Multimodal fusion consistently outperforms unimodal approaches, with reported F1 gains of 5-15% on benchmark datasets; however, no reviewed system has undergone prospective clinical validation, and reported accuracy figures should be interpreted as upper bounds on clinical utility. Critical open challenges include cross-cultural generalizability, longitudinal monitoring, interpretability, and ethical governance. This review reframes the field's central question from can AI detect depression? to under what conditions and with what governance can it be safely deployed?
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Wang2025Advances,
  author = {Liangguo Wang and Jiaqian Wu},
  title = {Advances in Artificial Intelligence-Based Depression Diagnosis: A Systematic Review},
  journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
  year = {2025},
  volume = {2},
  number = {3},
  pages = {148-156},
  doi = {10.62762/TETAI.2025.416797},
  url = {https://www.icck.org/article/abs/TETAI.2025.416797},
  abstract = {Depression affects approximately 280 million people worldwide, yet remains substantially underdiagnosed due to the limitations of clinical assessment tools. This systematic review synthesizes 38 studies (2013--2025) on AI-based depression detection across voice, facial expression, physiological signals, and social media modalities, following PRISMA 2020 guidelines. Multimodal fusion consistently outperforms unimodal approaches, with reported F1 gains of 5-15\% on benchmark datasets; however, no reviewed system has undergone prospective clinical validation, and reported accuracy figures should be interpreted as upper bounds on clinical utility. Critical open challenges include cross-cultural generalizability, longitudinal monitoring, interpretability, and ethical governance. This review reframes the field's central question from can AI detect depression? to under what conditions and with what governance can it be safely deployed?},
  keywords = {depression, artificial intelligence, multimodal features, deep learning, intelligent detection},
  issn = {3068-6652},
  publisher = {Institute of Central Computation and Knowledge}
}

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