IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v17y2025i3p107-d1603160.html
   My bibliography  Save this article

Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things

Author

Listed:
  • John Mulo

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

  • Hengshuo Liang

    (School of Engineering & Technology, University of Washington Tacoma, Tacoma, WA 98402, USA)

  • Mian Qian

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

  • Milon Biswas

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

  • Bharat Rawal

    (Department of Computer Science & Digital Technologies, Grambling State University, Grambling, LA 71245, USA)

  • Yifan Guo

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

  • Wei Yu

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

Abstract

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.

Suggested Citation

  • John Mulo & Hengshuo Liang & Mian Qian & Milon Biswas & Bharat Rawal & Yifan Guo & Wei Yu, 2025. "Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things," Future Internet, MDPI, vol. 17(3), pages 1-48, March.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:3:p:107-:d:1603160
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/17/3/107/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/17/3/107/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shirin Enshaeifar & Ahmed Zoha & Severin Skillman & Andreas Markides & Sahr Thomas Acton & Tarek Elsaleh & Mark Kenny & Helen Rostill & Ramin Nilforooshan & Payam Barnaghi, 2019. "Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-22, January.
    2. Sang Won Choi & Brian H. S. Kim, 2021. "Applying PCA to Deep Learning Forecasting Models for Predicting PM 2.5," Sustainability, MDPI, vol. 13(7), pages 1-30, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Longhui Fu & Qibang Wang & Jianhui Li & Huiran Jin & Zhen Zhen & Qingbin Wei, 2022. "Spatiotemporal Heterogeneity and the Key Influencing Factors of PM 2.5 and PM 10 in Heilongjiang, China from 2014 to 2018," IJERPH, MDPI, vol. 19(18), pages 1-20, September.
    2. Belfiore, Alessandra & Cuccurullo, Corrado & Aria, Massimo, 2022. "IoT in healthcare: A scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Jun Zhang & Shenghao Zhao & Chaonan Peng & Xianming Gong, 2022. "Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
    4. Yufei Fang & Zhiguang Shan, 2022. "How to Promote a Smart City Effectively? An Evaluation Model and Efficiency Analysis of Smart Cities in China," Sustainability, MDPI, vol. 14(11), pages 1-23, May.
    5. Paola Ortiz-Grisales & Julián Patiño-Murillo & Eduardo Duque-Grisales, 2021. "Comparative Study of Computational Models for Reducing Air Pollution through the Generation of Negative Ions," Sustainability, MDPI, vol. 13(13), pages 1-13, June.
    6. Irene Nandutu & Marcellin Atemkeng & Nokubonga Mgqatsa & Sakayo Toadoum Sari & Patrice Okouma & Rockefeller Rockefeller & Theophilus Ansah-Narh & Jean Louis Ebongue Kedieng Fendji & Franklin Tchakount, 2022. "Error Correction Based Deep Neural Networks for Modeling and Predicting South African Wildlife–Vehicle Collision Data," Mathematics, MDPI, vol. 10(21), pages 1-31, October.
    7. Jie Zhao & Linjiang Yuan & Kun Sun & Han Huang & Panbo Guan & Ce Jia, 2022. "Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks," Sustainability, MDPI, vol. 14(15), pages 1-18, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:17:y:2025:i:3:p:107-:d:1603160. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.