Threat to trust: A systematic review on Internet of medical things security

The Internet of Medical Things (IoMT) has transformed healthcare by enabling seamless communication among medical devices, supporting real-time monitoring, diagnostics, vital patient data tracking, improved patient care, disease prediction, early warning, and enhanced operational efficiency. Due to...

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Bibliographic Details
Published in:Journal of parallel and distributed computing Vol. 206; p. 105172
Main Authors: Shammar, Elham, Cui, Xiaohui, Zahary, Ammar, Alsamhi, Saeed Hamood, Al-qaness, Mohammed A.A.
Format: Journal Article
Language:English
Published: Elsevier Inc 01.12.2025
Subjects:
Attack
Blockchain
Decentralized healthcare
IoMT
Machine learning
Parallel and distributed computing
Security and privacy
Parallel and distributed computing
Security and privacy
Blockchain
Machine learning
Attack
Decentralized healthcare
IoMT
ISSN:0743-7315
Online Access:Get full text
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Summary:The Internet of Medical Things (IoMT) has transformed healthcare by enabling seamless communication among medical devices, supporting real-time monitoring, diagnostics, vital patient data tracking, improved patient care, disease prediction, early warning, and enhanced operational efficiency. Due to the sensitive nature of health-related data, the adoption of IoMT has raised significant privacy and security concerns, prompting comprehensive evaluation of IoMT security and making it a prime target for cyberattacks such as ransomware, denial-of-service (DoS) attacks, and malware. Securing IoMT requires efficient data processing across distributed systems to ensure both confidentiality and availability. Parallel and distributed computing can address scalability and performance challenges in IoMT security, particularly in enabling real-time monitoring and threat detection across multiple interconnected devices. This survey conducts a systematic literature review (SLR) of IoMT security to analyze key issues, categorize security threats, attack vectors, and vulnerabilities, and examine how emerging technologies such as blockchain, machine learning (ML), and physically unclonable functions (PUF) are strengthening IoMT security. This SLR reviews IoMT security research published between 2020 and 2024 to identify challenges and provide insights for future researchers and developers of new IoMT security models. This SLR offers practitioners and researchers guidance for developing reliable and resilient IoMT security systems in the decentralized healthcare industry. •A comprehensive overview of IoMT security, addressing issues, attacks, threats, and challenges.•Review the latest IoMT security research published between 2020 and 2024.•Highlight the limitations of current IoMT security research and propose potential solutions.•Providing a clear roadmap for future research and development in IoMT security.•Provide a structured approach for academics to investigate emerging gaps in IoMT security research.
ISSN:0743-7315
DOI:10.1016/j.jpdc.2025.105172