Healthcare Internet of Things (H-IoT): Current Trends, Future Prospects, Applications, Challenges, and Security Issues
Open accessAbstract
Advancements in Healthcare Internet of Things (H-IoT) systems have created new opportunities and solutions for healthcare services, including the remote treatment and monitoring of patients. In addition, the security and privacy of personal health data must be ensured during data transfer. Security breaches in H-IoT can have serious safety and legal implications. This comprehensive review provides insights about secured data accession by employing cryptographic platforms such as H-IoT in big data, H-IoT in blockchain, H-IoT in machine learning and deep learning, H-IoT in edge computing, and H-IoT in software-defined networks. With this information, this paper reveals solutions to mitigate threats caused by different kinds of attacks. The prevailing challenges in H-IoT systems, including security and scalability challenges, real-time operating challenges, resource constraints, latency, and power consumption challenges are also addressed. We also discuss in detail the current trends in H-IoT, such as remote patient monitoring and predictive analytics. Additionally, we have explored future prospects, such as leveraging health data for informed strategic planning. A critical analysis performed by highlighting the prevailing limitations in H-IoT systems is also presented. This paper will hopefully provide future researchers with in-depth insights into the selection of appropriate cryptographic measures to adopt an energy-efficient and resource-optimized healthcare system.
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AI applications in healthcare involve diagnostic assistance, medical image analysis, and predictive analytics for patient results, supporting healthcare providers in making data-driven decisions and reducing diagnostic mistakes.
“AI applications in healthcare involve diagnostic assistance, medical image analysis, and predictive analytics for patient results, therefore supporting healthcare providers in making data-driven decisions and reducing diagnostic mistakes [98,99].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [99]
Section 5.9 (page not available) describes an algorithm that analyzes medical images to aid diagnosis, while the Introduction and section 5.3 link health-data analysis to clinical decisions. Section 5.1 discusses predicting hospital visits and admissions, not patient outcomes. The cited passages do not establish that these applications reduce diagnostic mistakes. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI and ML are crucial in the modification of smart health systems, ensuring advanced data analysis and predictive modeling.
“AI and ML are crucial in the modification of smart health systems, ensuring advanced data analysis and predictive modeling.”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [99]
Federated learning allows data to persist within the boundaries of its origin and supplies a decentralized model training method that aligns with healthcare privacy regulations such as GDPR and HIPAA.
“By allowing data to persist within the boundaries of its origin (e.g., hospitals or IoT-enabled smart health devices), FL supplies a decentralized model training method that aligns well with rigid healthcare privacy regulations such as the GDPR and HIPAA [69].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [99]
Section 3.2 (page not provided) proposes a fog-based 'federal system near the devices' for security, but does not describe federated learning or say that training data remain at their origin. Searches for federated learning, GDPR, and HIPAA found no passage connecting that training method to either regulation. (LLM, conf=0.72)
Experimental — accuracy not yet measuredUser authentication and role-based access control are implemented via Laravel's built-in mechanisms.
“On the security side, user authentication and role-based access control [43] are implemented via Laravel's built-in mechanisms.”
From Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users (2025) · cites this paper as [43]
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- Authors
- Kumar, M., Kumar, A., Verma, S., Bhattacharya, P., Ghimire, D., Kim, S.-H., & Hosen, A. S. M. S.
- Affiliations
- MIT Art, Design and Technology University (IN)
- SRM Institute of Science and Technology (IN)
- Uttaranchal University (IN)
- Amity University (IN)
- Soongsil University (KR)
- Woosong University (KR)
- Corresponding author
- Kim, Seong-heum
- Hosen, A. S. M. Sanwar
- ORCID
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2023-04-28
- Volume
- 12
- Issue
- 9
- Pages
- 2050
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4367310283
- Cited by
- 199
- References
- 81
- Citations by year
- 2026: 41 · 2025: 79 · 2024: 58 · 2023: 21
- FWCI
- 46.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- Funders
- National Research Foundation of Korea (NRF) (2021R1G1A1009828)
- Woosong University Academic Research Fund, Korea (2021R1G1A1009828)
- Topics
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- Internet of Medical Things, Internet of Things security, patient data privacy, remote patient monitoring, blockchain, edge computing, software-defined networking, cryptographic security, predictive analytics, resource constraints, energy efficiency
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