ArticleCureus2025
An Internet of Medical Things Cyber Security Assessment Model (IoMT-CySAM).
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- IoT-Enabled Adaptive Traffic Management: A Multiagent Framework for Urban Mobility Optimisation.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Backgrounds E-health systems and particularly those based on connected medical devices, commonly known as Internet of Medical Things (IoMT) paradigm, are among the trends these days. As a transformative technology in the healthcare domain, it mainly enables real-time monitoring and seamless data exchange. However, given their complexity of architecture, their ubiquitous nature, and their resource limitations, ensuring the privacy and protecting user's data in such systems remains issue of concern. New security risks within Internet of Things (IoT)/IoMT-based e-health applications have emerged. Methods The current research work relies on an in-depth study of IoMT architectures addressing their technical foundations as well as their associated security considerations. From a security standpoint, common vulnerabilities, threats, and associated risks are identified, and state-of-the-art mitigation strategies, mainly standard risk management frameworks, are evaluated. A comparative analysis was conducted on existing solutions, discussing their suitability in addressing the identified concerns. Results The conducted study reveals that, in general contexts, applying and conducting risk management processes are not easy tasks (seem to be confusing and prone to errors), especially within heterogeneous and complex systems such as IoMT applications. There is a strong need for automatic solutions to simplify the complexity of the application of different models and processes. Automation and tools are considered among important factors to ensure the success of any proposal. We present, in this manuscript, our framework designed to handle this issue and introduce our system called IoMT-CySAM (Internet of Medical Things - Cyber Security Assessment Model), a main part of our research work in this context. IoMT-CySAM allows evaluating trustworthiness and managing cyber risks within IoMT environments. Conclusions As the deployment of IoMT systems faces critical security and privacy challenges, automated risk management solutions are highly required to handle the issues. The IoMT-CySAM, as a context-aware and adaptive solution, is defined to help with trustworthiness evaluation and security risk management within IoMT environments. Moving forward, research should prioritize automated reliable solutions that ensure both effective protection and operational efficiency.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.