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

John Mulo, Hengshuo Liang, Mian Qian, Milon Biswas, Bharat Rawal, Yifan Guo and Wei Yu ()
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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

Future Internet, 2025, vol. 17, issue 3, 1-48

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.

Keywords: deep learning (DL); Internet of Medical Things (IoMT); DL applications; smart healthcare (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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