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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
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