New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities
Open accessAbstract
Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively.
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Combining AI and edge computing allows healthcare providers to create innovative solutions that improve public health outcomes, lower healthcare costs, and improve patient experience.
“By combining the strengths of AI and edge computing, healthcare providers can create innovative solutions that improve public health outcomes, lower healthcare costs, and improve the overall patient experience [9,10].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [9]
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- Authors
- Kamruzzaman, M. M., Alrashdi, I., & Alqazzaz, A.
- Affiliations
- Jouf University (SA)
- University of Bisha (SA)
- Corresponding author
- Kamruzzaman, M. M.
- Venue
- Journal of Healthcare Engineering
- Publisher
- Wiley
- Published
- 2022-02-23
- Volume
- 2022
- Pages
- 1-14
- ISSN
- 2040-2295 (ISSN-L)
- 2040-2309
- PMC
- PMC8890828
- OpenAlex
- W4213435163
- Cited by
- 68
- References
- 55
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 17 · 2023: 31 · 2022: 13
- FWCI
- 7.32
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2023-10-11)
- Open access
- hybrid
- doi.org
- Locations
- Journal of Healthcare Engineering (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Jouf University (DSR-2021-02–0333)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- edge AI, Internet of Medical Things, connected healthcare, smart cities, cloud computing, 6G, smart sensors, health care challenges, model validation
- MeSH terms
- Cloud Computing, Internet of Things, Artificial Intelligence, Cities, Delivery of Health Care, Humans
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