ReviewMolecular psychiatry2024
Applications of artificial intelligence-machine learning for detection of stress: a critical overview.
Review in Molecular psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Advantages and disadvantages of artificial intelligence in the prediction and prevention of suicide.Postepy psychiatrii neurologii · 2026Review
- Digital Mental Health Through an Intersectional Lens: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- From predictive algorithms to generative intelligence: a decadal bibliometric mapping of global research frontiers in AI-driven mental health (2016-2025).Australian journal of psychology · 2026Review
- Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based classification method.European journal of psychotraumatology · 2025Article
- Practical AI application in psychiatry: historical review and future directions.Molecular psychiatry · 2025Review
- Stress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research Perspectives.Journal of healthcare informatics research · 2025Review
- A Novel Optimized Hybrid Deep Learning Framework for Mental Stress Detection Using Electroencephalography.Brain sciences · 2025Article
- AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts.Brain informatics · 2025Article
- From lab to real-life: A three-stage validation of wearable technology for stress monitoring.MethodsX · 2025Article
- Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned.Frontiers in psychiatry · 2025Article
- A systematic review of machine learning findings in PTSD and their relationships with theoretical models.Nature. Mental health · 2025Article
- Intimate partner violence and stress-related disorders: from epigenomics to resilience.Frontiers in global women's health · 2025Review
- Epitranscriptomic modifications for enhancing abiotic stress resistance in plants.Frontiers in plant science · 2025Review
- A XGBoost-Based Prediction Method for Meat Sheep Transport Stress Using Wearable Photoelectric Sensors and Infrared Thermometry.Sensors (Basel, Switzerland) · 2024Article
- QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals.Scientific reports · 2024Article
- Framework for Microdosing Odors in Virtual Reality for Psychophysiological Stress Training.Sensors (Basel, Switzerland) · 2024Article
- A More Connected Future: How Social Connection, Interdisciplinary Approaches, and New Technology Will Shape the Affective Science of Loneliness, a Commentary on the Special Issue.Affective science · 2024Review
- Development of an Artificial Intelligence-Based Tailored Mobile Intervention for Nurse Burnout: Single-Arm Trial.Journal of medical Internet research · 2024Article
- Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.Cell metabolism · 2024Review
- Multi-physiological signal fusion for objective emotion recognition in educational human-computer interaction.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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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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.