Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.1
2.8
Journals
Future Internet
Special Issues
The Future Internet of Medical Things II
share announcement

The Future Internet of Medical Things II

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 63032

Special Issue Editor

Dr. Matthew Pediaditis

E-Mail Website1 Website2
Guest Editor
Institute of Computer Science (ICS), Foundation for Research and Technology-Hellas (FORTH), GR-70013 Heraklion, Greece
Interests: AI in healthcare; deep learning for patient monitoring; wearable devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Medical Things (IoMT) is often declared as the future of healthcare. It is an amalgamation of medical devices and applications that are connected to health information technology systems using state-of-the-art communication technologies.

IoMT consists of smart devices, such as wearables and medical monitors, designed for healthcare purposes to be used on the human body, at home, or in the community, as well as in clinical settings. The on-body segment can be divided into consumer health wearables and medical wearables. At home, we can find personal emergency-response systems, remote patient monitoring, and virtual doctors. In the community, we have mobility services for health tracking during transit, emergency-response intelligence, point-of-care devices, and logistics involving the transport and delivery of healthcare goods and services. Finally, in the clinical domain, IoMT is used for clinical (nursing, smart health monitoring, and more) and administrative functions (asset and personnel management, patient flow management, inventory management, etc.)

Moreover, IoMT includes cloud-based platforms hosting applications that will bring together data from all aforementioned devices that have been obscured from each other throughout the healthcare industry so far. IoMT will create useful data to bring healthcare insights and professionals together in order to advance medicine and human health.

The aim of this Special Issue is to highlight the most recent innovations in IoMT technologies able to provide secure, unobtrusive, and continuous health monitoring and analytic services.

The topics of this Special Issue include but are not limited to the following:

  • State-of-the-art IoMT health monitoring and sensing paradigms;
  • Data collection, integration, and interpretation in IoMT;
  • Unobtrusive health monitoring with IoMT;
  • IoMT security solutions regarding sensitive medical data;
  • IoMT network architectures and connectivity;
  • Edge computing in the domain of IoMT;
  • Ultra-low-power IoMT devices;
  • IoMT and Healthcare 4.0.;
  • IoMT for health emergency situations;
  • Interoperability and data exchange issues.

Dr. Matthew Pediaditis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

22 pages, 8329 KiB  
Article
Prototyping a Hyperledger Fabric-Based Security Architecture for IoMT-Based Health Monitoring Systems
by Filippos Pelekoudas-Oikonomou, José C. Ribeiro, Georgios Mantas, Georgia Sakellari and Jonathan Gonzalez
Future Internet 2023, 15(9), 308; https://doi.org/10.3390/fi15090308 - 11 Sep 2023
Cited by 8 | Viewed by 2551
Abstract
The Internet of Medical Things (IoMT) has risen significantly in recent years and has provided better quality of life by enabling IoMT-based health monitoring systems. Despite that fact, innovative security mechanisms are required to meet the security concerns of such systems effectively and [...] Read more.
The Internet of Medical Things (IoMT) has risen significantly in recent years and has provided better quality of life by enabling IoMT-based health monitoring systems. Despite that fact, innovative security mechanisms are required to meet the security concerns of such systems effectively and efficiently. Additionally, the industry and the research community have anticipated that blockchain technology will be a disruptive technology that will be able to be integrated into innovative security solutions for IoMT networks since it has the potential to play a big role in: (a) enabling secure data transmission, (b) ensuring IoMT device security, and (c) enabling tamper-proof data storage. Therefore, the purpose of this research work is to design a novel lightweight blockchain-based security architecture for IoMT-based health monitoring systems leveraging the features of the Hyperledger Fabric (HF) Platform, its utilities. and its lightweight blockchain nature in order to: (i) ensure entity authentication, (ii) ensure data confidentiality, and (iii) enable a more energy-efficient blockchain-based security architecture for IoMT-based health monitoring systems while considering the limited resources of IoMT gateways. While security mechanisms for IoT utilizing HF do exist, to the best of our knowledge there is no specific HF-based architecture for IoMT-based health monitoring systems. Full article
(This article belongs to the Special Issue The Future Internet of Medical Things II)
►▼ Show Figures

Figure 1

21 pages, 548 KiB  
Article
Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)
by Anand Singh Rajawat, S. B. Goyal, Pradeep Bedi, Tony Jan, Md Whaiduzzaman and Mukesh Prasad
Future Internet 2023, 15(8), 271; https://doi.org/10.3390/fi15080271 - 15 Aug 2023
Cited by 23 | Viewed by 3805
Abstract
Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more [...] Read more.
Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways. The IoMT devices; however, often do not have sufficient computing resources onboard for service and security assurance while the medical services handle large quantities of sensitive and private health-related data. This leads to several research problems on how to improve security in IoMT systems. This paper focuses on quantum machine learning to assess security vulnerabilities in IoMT systems. This paper provides a comprehensive review of both traditional and quantum machine learning techniques in IoMT vulnerability assessment. This paper also proposes an innovative fused semi-supervised learning model, which is compared to the state-of-the-art traditional and quantum machine learning in an extensive experiment. The experiment shows the competitive performance of the proposed model against the state-of-the-art models and also highlights the usefulness of quantum machine learning in IoMT security assessments and its future applications. Full article
(This article belongs to the Special Issue The Future Internet of Medical Things II)
►▼ Show Figures

Figure 1

25 pages, 1665 KiB  
Article
The mPOC Framework: An Autonomous Outbreak Prediction and Monitoring Platform Based on Wearable IoMT Approach
by Sasan Adibi
Future Internet 2023, 15(8), 257; https://doi.org/10.3390/fi15080257 - 30 Jul 2023
Cited by 5 | Viewed by 3393
Abstract
This paper presents the mHealth Predictive Outbreak for COVID-19 (mPOC) framework, an autonomous platform based on wearable Internet of Medical Things (IoMT) devices for outbreak prediction and monitoring. It utilizes real-time physiological and environmental data to assess user risk. The framework incorporates the [...] Read more.
This paper presents the mHealth Predictive Outbreak for COVID-19 (mPOC) framework, an autonomous platform based on wearable Internet of Medical Things (IoMT) devices for outbreak prediction and monitoring. It utilizes real-time physiological and environmental data to assess user risk. The framework incorporates the analysis of psychological and user-centric data, adopting a combination of top-down and bottom-up approaches. The mPOC mechanism utilizes the bidirectional Mobile Health (mHealth) Disaster Recovery System (mDRS) and employs an intelligent algorithm to calculate the Predictive Exposure Index (PEI) and Deterioration Risk Index (DRI). These indices trigger warnings to users based on adaptive threshold criteria and provide updates to the Outbreak Tracking Center (OTC). This paper provides a comprehensive description and analysis of the framework’s mechanisms and algorithms, complemented by the performance accuracy evaluation. By leveraging wearable IoMT devices, the mPOC framework showcases its potential in disease prevention and control during pandemics, offering timely alerts and vital information to healthcare professionals and individuals to mitigate outbreaks’ impact. Full article
(This article belongs to the Special Issue The Future Internet of Medical Things II)
►▼ Show Figures

Figure 1

Review

Jump to: Research

21 pages, 3950 KiB  
Review
Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’
by Peng Zhang, Jiayu Shi and Maged N. Kamel Boulos
Future Internet 2024, 16(12), 462; https://doi.org/10.3390/fi16120462 - 9 Dec 2024
Cited by 3 | Viewed by 3647
Abstract
The rapid development of specific-purpose Large Language Models (LLMs), such as Med-PaLM, MEDITRON-70B, and Med-Gemini, has significantly impacted healthcare, offering unprecedented capabilities in clinical decision support, diagnostics, and personalized health monitoring. This paper reviews the advancements in medicine-specific LLMs, the integration of Retrieval-Augmented [...] Read more.
The rapid development of specific-purpose Large Language Models (LLMs), such as Med-PaLM, MEDITRON-70B, and Med-Gemini, has significantly impacted healthcare, offering unprecedented capabilities in clinical decision support, diagnostics, and personalized health monitoring. This paper reviews the advancements in medicine-specific LLMs, the integration of Retrieval-Augmented Generation (RAG) and prompt engineering, and their applications in improving diagnostic accuracy and educational utility. Despite the potential, these technologies present challenges, including bias, hallucinations, and the need for robust safety protocols. The paper also discusses the regulatory and ethical considerations necessary for integrating these models into mainstream healthcare. By examining current studies and developments, this paper aims to provide a comprehensive overview of the state of LLMs in medicine and highlight the future directions for research and application. The study concludes that while LLMs hold immense potential, their safe and effective integration into clinical practice requires rigorous testing, ongoing evaluation, and continuous collaboration among stakeholders. Full article
(This article belongs to the Special Issue The Future Internet of Medical Things II)
►▼ Show Figures

Figure 1

22 pages, 945 KiB  
Review
Resilience in the Internet of Medical Things: A Review and Case Study
by Vikas Tomer, Sachin Sharma and Mark Davis
Future Internet 2024, 16(11), 430; https://doi.org/10.3390/fi16110430 - 20 Nov 2024
Viewed by 1435
Abstract
The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT), is still in its early stages of development. Challenges that are inherent to IoT, persist in IoMT as well. The major focus is on data transmission within the healthcare [...] Read more.
The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT), is still in its early stages of development. Challenges that are inherent to IoT, persist in IoMT as well. The major focus is on data transmission within the healthcare domain due to its profound impact on health and public well-being. Issues such as latency, bandwidth constraints, and concerns regarding security and privacy are critical in IoMT owing to the sensitive nature of patient data, including patient identity and health status. Numerous forms of cyber-attacks pose threats to IoMT networks, making the reliable and secure transmission of critical medical data a challenging task. Several other situations, such as natural disasters, war, construction works, etc., can cause IoMT networks to become unavailable and fail to transmit the data. The first step in these situations is to recover from failure as quickly as possible, resume the data transfer, and detect the cause of faults, failures, and errors. Several solutions exist in the literature to make the IoMT resilient to failure. However, no single approach proposed in the literature can simultaneously protect the IoMT networks from various attacks, failures, and faults. This paper begins with a detailed description of IoMT and its applications. It considers the underlying requirements of resilience for IoMT networks, such as monitoring, control, diagnosis, and recovery. This paper comprehensively analyzes existing research efforts to provide IoMT network resilience against diverse causes. After investigating several research proposals, we identify that the combination of software-defined networks (SDNs), machine learning (ML), and microservices architecture (MSA) has the capabilities to fulfill the requirements for achieving resilience in the IoMT networks. It mainly focuses on the analysis of technologies, such as SDN, ML, and MSA, separately, for meeting the resilience requirements in the IoMT networks. SDN can be used for monitoring and control, and ML can be used for anomaly detection and diagnosis, whereas MSA can be used for bringing distributed functionality and recovery into the IoMT networks. This paper provides a case study that describes the remote patient monitoring (RPM) of a heart patient in IoMT networks. It covers the different failure scenarios in IoMT infrastructure. Finally, we provide a proposed methodology that elaborates how distributed functionality can be achieved during these failures using machine learning, software-defined networks, and microservices technologies. Full article
(This article belongs to the Special Issue The Future Internet of Medical Things II)
►▼ Show Figures

Figure 1

15 pages, 644 KiB  
Review
Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges
by Peng Zhang and Maged N. Kamel Boulos
Future Internet 2023, 15(9), 286; https://doi.org/10.3390/fi15090286 - 24 Aug 2023
Cited by 140 | Viewed by 47749
Abstract
Generative AI (artificial intelligence) refers to algorithms and models, such as OpenAI’s ChatGPT, that can be prompted to generate various types of content. In this narrative review, we present a selection of representative examples of generative AI applications in medicine and healthcare. We [...] Read more.
Generative AI (artificial intelligence) refers to algorithms and models, such as OpenAI’s ChatGPT, that can be prompted to generate various types of content. In this narrative review, we present a selection of representative examples of generative AI applications in medicine and healthcare. We then briefly discuss some associated issues, such as trust, veracity, clinical safety and reliability, privacy, copyrights, ownership, and opportunities, e.g., AI-driven conversational user interfaces for friendlier human-computer interaction. We conclude that generative AI will play an increasingly important role in medicine and healthcare as it further evolves and gets better tailored to the unique settings and requirements of the medical domain and as the laws, policies and regulatory frameworks surrounding its use start taking shape. Full article
(This article belongs to the Special Issue The Future Internet of Medical Things II)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop