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

Parveen, Saba and Heyat, Md Belal Bin and Tariq, Umair and Akhtar, Faijan and Zeeshan, Hafiz Muhammad and Appiah, Seth Christopher Yaw and Zhou, Shang-Ming and Lei, Huang (2025) AI-driven biomedical perspectives on mental fatigue in the post-COVID-19 Era: trends, research gaps, and future directions. Journal of Big Data, 12 (1). ISSN 2196-1115

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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.

Item Type:	Article
Identification Number:	10.1186/s40537-025-01200-y
Dates:	Date Event 20 May 2025 Accepted 9 August 2025 Published Online
Uncontrolled Keywords:	ds: Psychological disorder, Mental fatigue, Artificial intelligence, COVID-19, Signal, Bibliometric analysis, Stress, Neurological disorder, Machine learning, Deep learning
Subjects:	CAH00 - multidisciplinary > CAH00-00 - multidisciplinary > CAH00-00-00 - multidisciplinary
Divisions:	Arts > Art and Design
Depositing User:	Gemma Tonks
Date Deposited:	19 Aug 2025 12:47
Last Modified:	19 Aug 2025 12:47
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16600

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