Trends in IoT based solutions for health care: Moving AI to the edge
Open accessAbstract
In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet of things devices that are heavily changing the way healthcare is approached worldwide. In this context, a combination of Cloud and IoT architectures is often exploited to make smart healthcare systems capable of supporting near realtime applications when processing and performing Artificial Intelligence on the huge amount of data produced by wearable sensor networks. Anyway, the response time and the availability of cloud based systems, together with security and privacy, still represent critical issues that prevents Internet of Medical Things (IoMT) devices and architectures from being a reliable and effective solution to the aim. Lately, there is a growing interest towards architectures and approaches that exploit Edge and Fog computing as an answer to compensate the weaknesses of the cloud. In this paper, we propose a short review about the general use of IoT solutions in health care, starting from early health monitoring solutions from wearable sensors up to a discussion about the latest trends in fog/edge computing for smart health.
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The computing paradigm is currently shifting from cloud computing to end-edge-cloud computing, which supports AI evolving from a centralized AI deployment to a distributed artificial intelligence (DAI).
“The computing paradigm is currently shifting from cloud computing to end-edge-cloud computing, which also supports AI evolving from a centralized AI deployment to a distributed artificial intelligence (DAI).”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [41]
The Introduction describes growing cooperation among cloud, fog and edge computing and the distribution of AI inference across those levels. Discussion and trends reports an increasing trend toward such cooperation in healthcare IoT, while noting that most applications still offload deep analysis to the cloud. The claim extends this healthcare-specific trend to computing and AI generally and presents the shift more definitively than the paper does. (LLM, conf=0.84)
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- Authors
- Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M.
- Affiliations
- University of Salerno (IT)
- Corresponding author
- Greco, Luca
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 135
- Pages
- 346-353
- ISSN
- 0167-8655 (ISSN-L)
- PMC
- PMC7217772
- OpenAlex
- W3025245379
- Cited by
- 318
- References
- 61
- Citations by year
- 2026: 15 · 2025: 36 · 2024: 53 · 2023: 72 · 2022: 65 · 2021: 67 · 2020: 10
- FWCI
- 25.14
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Pattern Recognition Letters (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- 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 › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- Internet of Medical Things, edge computing, fog computing, wearable sensors, smart health monitoring, artificial intelligence in healthcare, response time, privacy and security, near real-time processing
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