Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions.

5G Healthcare 4.0 Internet of Medical Things artificial intelligence distributed edge computing e-health

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
25 Aug 2023
Historique:
received: 05 07 2023
revised: 15 08 2023
accepted: 23 08 2023
medline: 11 9 2023
pubmed: 9 9 2023
entrez: 9 9 2023
Statut: epublish

Résumé

Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient's characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.

Identifiants

pubmed: 37687891
pii: s23177435
doi: 10.3390/s23177435
pmc: PMC10490658
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

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Auteurs

Manar Osama (M)

Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt.

Abdelhamied A Ateya (AA)

EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt.

Mohammed S Sayed (MS)

Department of Electronics and Communication Engineering, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt.
Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt.

Mohamed Hammad (M)

EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt.

Paweł Pławiak (P)

Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland.
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland.

Ahmed A Abd El-Latif (AA)

EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shibin El Kom 32511, Egypt.

Rania A Elsayed (RA)

Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt.

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