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Intelligent internet of medical things for depression: current advancements, challenges, and trends

Md Belal Bin Heyat, Deepak Adhikari, Faijan Akhtar, Saba Parveen, Hafiz Muhammad Zeeshan, Hadaate Ullah, Yun-Hsuan Chen, Lu Wang et Mohamad Sawan

Article de revue (2025)

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Abstract

We investigated the fusion of the Intelligent Internet of Medical Things (IIoMT) with depression management, aiming to autonomously identify, monitor, and offer accurate advice without direct professional intervention. Addressing pivotal questions regarding IIoMT’s role in depression identification, its correlation with stress and anxiety, the impact of machine learning (ML) and deep learning (DL) on depressive disorders, and the challenges and potential prospects of integrating depression management with IIoMT, this research offers significant contributions. It integrates artificial intelligence (AI) and Internet of Things (IoT) paradigms to expand depression studies, highlighting data science modeling’s practical application for intelligent service delivery in real-world settings, emphasizing the benefits of data science within IoT. Furthermore, it outlines an IIoMT architecture for gathering, analyzing, and preempting depressive disorders, employing advanced analytics to enhance application intelligence. The study also identifies current challenges, future research trajectories, and potential solutions within this domain, contributing to the scientific understanding and application of IIoMT in depression management. It evaluates 168 closely related articles from various databases, including Web of Science (WoS) and Google Scholar, after the rejection of repeated articles and books. The research shows that there is 48% growth in research articles, mainly focusing on symptoms, detection, and classification. Similarly, most research is being conducted in the United States of America, and the trend is increasing in other countries around the globe. These results suggest the essence of automated detection, monitoring, and suggestions for handling depression.

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Département: Département de génie électrique
Centre de recherche: Autre
Organismes subventionnaires: Westlake University
Numéro de subvention: 10318A992001
URL de PolyPublie: https://publications.polymtl.ca/62688/
Titre de la revue: International Journal of Intelligent Systems (vol. 2025)
Maison d'édition: Wiley
DOI: 10.1155/int/6801530
URL officielle: https://doi.org/10.1155/int/6801530
Date du dépôt: 13 févr. 2025 08:55
Dernière modification: 21 oct. 2025 14:41
Citer en APA 7: Heyat, M. B. B., Adhikari, D., Akhtar, F., Parveen, S., Zeeshan, H. M., Ullah, H., Chen, Y.-H., Wang, L., & Sawan, M. (2025). Intelligent internet of medical things for depression: current advancements, challenges, and trends. International Journal of Intelligent Systems, 2025, 6801530 (35 pages). https://doi.org/10.1155/int/6801530

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