Applications of artificial intelligence−machine learning for detection of stress: a critical overview
Abstract
Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence (SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.
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No previous study has incorporated AI to provide tailored programs to users to reduce burnout.
“To the best of our knowledge, no previous study has incorporated AI to provide tailored programs to users to reduce burnout.”
From Development of an artificial intelligence-based tailored mobile intervention for nurse burnout: single-arm trial (2024) · cites this paper as [61]
Several studies have used AI to predict or manage burnout based on physiological indicators or to predict the degree of burnout.
“Several studies have used AI to predict or manage burnout based on physiological indicators or to predict the degree of burnout [61-64].”
From Development of an artificial intelligence-based tailored mobile intervention for nurse burnout: single-arm trial (2024) · cites this paper as [61]
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Muse, E. D., & Topol, E. J. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.02.002 · Cited by 50 · see citing works
Cho, A., Cha, C., & Baek, G. (2024) · Journal of Medical Internet Research · doi:10.2196/54029 · Cited by 29 · see citing works
- Authors
- Mentis, A.-F. A., Lee, D., & Roussos, P.
- Affiliations
- National and Kapodistrian University of Athens (GR)
- Icahn School of Medicine at Mount Sinai (US)
- James J. Peters VA Medical Center (US)
- Corresponding author
- Mentis, Alexios-Fotios A.
- Venue
- Molecular Psychiatry
- Published
- 2023-04-05
- Volume
- 29
- Issue
- 6
- Pages
- 1882-1894
- ISSN
- 1359-4184 (ISSN-L)
- 1476-5578
- OpenAlex
- W4362641063
- Cited by
- 75
- References
- 140
- Citations by year
- 2026: 16 · 2025: 31 · 2024: 22 · 2023: 6
- FWCI
- 3.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Molecular Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- stress detection, psychological distress, post-traumatic stress disorder, machine learning, artificial intelligence, swarm intelligence, ensemble learning, stress prediction, chronic stress, privacy preservation, diagnosis
- MeSH terms
- Machine Learning, Artificial Intelligence, Brain, Humans, Stress Disorders, Post-Traumatic, Stress, Psychological
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