Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things
Integrating deep learning (DL) with the Internet of Medical Things (IoMT) is a paradigm shift in modern healthcare, offering enormous opportunities for patient care, diagnostics, and treatment. Implementing DL with IoMT has the potential to deliver better diagnosis, treatment, and patient management...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/3/107 |
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| author | John Mulo Hengshuo Liang Mian Qian Milon Biswas Bharat Rawal Yifan Guo Wei Yu |
| author_facet | John Mulo Hengshuo Liang Mian Qian Milon Biswas Bharat Rawal Yifan Guo Wei Yu |
| author_sort | John Mulo |
| collection | DOAJ |
| description | Integrating deep learning (DL) with the Internet of Medical Things (IoMT) is a paradigm shift in modern healthcare, offering enormous opportunities for patient care, diagnostics, and treatment. Implementing DL with IoMT has the potential to deliver better diagnosis, treatment, and patient management. However, the practical implementation has challenges, including data quality, privacy, interoperability, and limited computational resources. This survey article provides a conceptual IoMT framework for healthcare, synthesizes and identifies the state-of-the-art solutions that tackle the challenges of the current applications of DL, and analyzes existing limitations and potential future developments. Through an analysis of case studies and real-world implementations, this work provides insights into best practices and lessons learned, including the importance of robust data preprocessing, integration with legacy systems, and human-centric design. Finally, we outline future research directions, emphasizing the development of transparent, scalable, and privacy-preserving DL models to realize the full potential of IoMT in healthcare. This survey aims to serve as a foundational reference for researchers and practitioners seeking to navigate the challenges and harness the opportunities in this rapidly evolving field. |
| format | Article |
| id | doaj-art-d0fd6f21aff74f25a54aa8ec955bff1e |
| institution | DOAJ |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-d0fd6f21aff74f25a54aa8ec955bff1e2025-08-20T02:42:30ZengMDPI AGFuture Internet1999-59032025-03-0117310710.3390/fi17030107Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical ThingsJohn Mulo0Hengshuo Liang1Mian Qian2Milon Biswas3Bharat Rawal4Yifan Guo5Wei Yu6Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USASchool of Engineering & Technology, University of Washington Tacoma, Tacoma, WA 98402, USADepartment of Computer and Information Sciences, Towson University, Towson, MD 21252, USADepartment of Computer and Information Sciences, Towson University, Towson, MD 21252, USADepartment of Computer Science & Digital Technologies, Grambling State University, Grambling, LA 71245, USADepartment of Computer and Information Sciences, Towson University, Towson, MD 21252, USADepartment of Computer and Information Sciences, Towson University, Towson, MD 21252, USAIntegrating deep learning (DL) with the Internet of Medical Things (IoMT) is a paradigm shift in modern healthcare, offering enormous opportunities for patient care, diagnostics, and treatment. Implementing DL with IoMT has the potential to deliver better diagnosis, treatment, and patient management. However, the practical implementation has challenges, including data quality, privacy, interoperability, and limited computational resources. This survey article provides a conceptual IoMT framework for healthcare, synthesizes and identifies the state-of-the-art solutions that tackle the challenges of the current applications of DL, and analyzes existing limitations and potential future developments. Through an analysis of case studies and real-world implementations, this work provides insights into best practices and lessons learned, including the importance of robust data preprocessing, integration with legacy systems, and human-centric design. Finally, we outline future research directions, emphasizing the development of transparent, scalable, and privacy-preserving DL models to realize the full potential of IoMT in healthcare. This survey aims to serve as a foundational reference for researchers and practitioners seeking to navigate the challenges and harness the opportunities in this rapidly evolving field.https://www.mdpi.com/1999-5903/17/3/107deep learning (DL)Internet of Medical Things (IoMT)DL applicationssmart healthcare |
| spellingShingle | John Mulo Hengshuo Liang Mian Qian Milon Biswas Bharat Rawal Yifan Guo Wei Yu Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things Future Internet deep learning (DL) Internet of Medical Things (IoMT) DL applications smart healthcare |
| title | Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things |
| title_full | Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things |
| title_fullStr | Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things |
| title_full_unstemmed | Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things |
| title_short | Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things |
| title_sort | navigating challenges and harnessing opportunities deep learning applications in internet of medical things |
| topic | deep learning (DL) Internet of Medical Things (IoMT) DL applications smart healthcare |
| url | https://www.mdpi.com/1999-5903/17/3/107 |
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