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

John Mulo,

Hengshuo Liang, Mian Qian

et al.

Future Internet, Journal Year: 2025, Volume and Issue: 17(3), P. 107 - 107

Published: March 1, 2025

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 IoMT has potential to deliver better diagnosis, treatment, management. However, practical implementation challenges, including data quality, privacy, interoperability, limited computational resources. This survey article provides conceptual framework synthesizes identifies state-of-the-art solutions that tackle challenges current applications DL, analyzes existing limitations future developments. Through an analysis case studies real-world implementations, this work insights into best practices lessons learned, importance robust preprocessing, integration legacy systems, human-centric design. Finally, we outline research directions, emphasizing development transparent, scalable, privacy-preserving models realize full healthcare. aims serve as foundational reference researchers practitioners seeking navigate harness rapidly evolving field.

Language: Английский

WCFormer: An interpretable deep learning framework for heart sound signal analysis and automated diagnosis of cardiovascular diseases DOI

Suiyan Wang,

Junhui Hu,

Yanwei Du

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127238 - 127238

Published: March 1, 2025

Language: Английский

Citations

1
Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things DOI Creative Commons

John Mulo,

Hengshuo Liang, Mian Qian

et al.

Future Internet, Journal Year: 2025, Volume and Issue: 17(3), P. 107 - 107

Published: March 1, 2025

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 IoMT has potential to deliver better diagnosis, treatment, management. However, practical implementation challenges, including data quality, privacy, interoperability, limited computational resources. This survey article provides conceptual framework synthesizes identifies state-of-the-art solutions that tackle challenges current applications DL, analyzes existing limitations future developments. Through an analysis case studies real-world implementations, this work insights into best practices lessons learned, importance robust preprocessing, integration legacy systems, human-centric design. Finally, we outline research directions, emphasizing development transparent, scalable, privacy-preserving models realize full healthcare. aims serve as foundational reference researchers practitioners seeking navigate harness rapidly evolving field.

Language: Английский

Citations

0