What is the clinical evidence from this study?

Study design: Review. Population: Mental fatigue. Intervention: Artificial Intelligence (Machine Learning and Deep Learning).

What does this research mean for the field?

Artificial intelligence techniques, including machine learning and deep learning, demonstrate significant potential for detecting, quantifying, and monitoring mental fatigue using physiological data. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

August 9, 2025Journal Of Big Data4 citationsOpen Access

AI-driven biomedical perspectives on mental fatigue in the post-COVID-19 Era: trends, research gaps, and future directions

SPSaba ParveenMahindra Group (India)MHMd Belal Bin HeyatWestlake University UTUmair TariqBirmingham City University

Key Result

Artificial intelligence techniques, including machine learning and deep learning, show encouraging potential in detecting and monitoring mental fatigue through physiological data.

Structured PICO

Population

182 peer-reviewed articles examining mental fatigue or related psychological disorders in the context of COVID-19, particularly those including biomedical, neurological, or artificial intelligence approaches.

Intervention

Artificial intelligence techniques (machine learning, deep learning) and bio-signal analysis for the detection and monitoring of mental fatigue.

This systematic review highlights the exacerbation of mental fatigue during the COVID-19 pandemic and the potential of AI and bio-signals to improve its detection and monitoring.

Limitations

Current methodologies often rely on subjective self-reports or simplistic metrics
Diversity of datasets and variability of fatigue manifestations across individuals
Limited use of explainable AI
Lack of real-world model validation
Inconsistencies in data collection protocols

Abstract

Mental fatigue is a complex condition arising from various neurological processes and influenced by external factors such as stress and cognitive demands. This comprehensive review elucidates the primary neurological mechanisms underlying mental fatigue, particularly emphasizing how it was elevated or otherwise affected during the COVID-19 pandemic. We explore the intricate relationship between prolonged cognitive tasks, chronic stress, and the development of mental fatigue, emphasizing the impacts that mental fatigue has on mental health across diverse populations. Utilizing advanced artificial intelligence techniques, including machine learning and deep learning, this study identifies and quantifies the patterns of mental fatigue. The innovative approach deployed in this study enhances our understanding of the complex interplay between mental fatigue and psychological disorders, uncovering potential predisposing factors and underlying mechanisms. A thorough bibliometric analysis highlights global research trends, key contributors, and emerging interdisciplinary methods in mental fatigue research. This paper identifies gaps in knowledge and methodological challenges. It proposes promising avenues for future investigations that emphasize multidisciplinary approaches and the development of novel diagnostic and treatment tools tailored to address mental fatigue. By integrating insights from neurological studies with the psychological implications of mental fatigue, this study aims to inform better interventions to improve mental health outcomes. Our findings have significant implications for healthcare professionals, researchers, and policymakers working to mitigate the impact of mental fatigue in various contexts.

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