Medical Things Research Articles

Artificial Intelligence and Machine Learning Techniques for Anomaly Detection and Threat Mitigation in Cloud-Connected Medical Devices

The Internet of Medical Things (IoMT) has begun functioning like this: improved patient monitoring and an easily accessible digital data warehouse. Despite that, this methodology of the internet will potentially have a counter balance which risks for patient data might involve hacking, data theft, and unauthorized access that may contain great consequences for patient privacy and safety. This article examines the possibility of utilizing new AI technology, including inter alia deep learning, unsupervised learning, and ensemble learning to further boost anomaly detection and threat management in connected cloud medical systems. Many old rules and approaches based on statistics lose relevancy versus the dynamics and unpredictability of modern cyberattacks. Identification of anomalies in cyber security is nearly unavoidable, and it should be the first and the last reaction for detecting irregularities in behavior that may indicate undesirable acts or attacks. The paper aims at understanding how AI/ML approaches can give more sophisticated and versatile interventions for finding out anomalies in cloud-attached medical machines. Moreover, this research details robust AI/ML methods such as the adversarial machine learning and reinforcement learning for a perfect threat mitigation. These techniques which activates machine learning models to learn from data continuing to adjust to new evolving threats and then to establish intelligent and proactive threat response systems. The data experiment, which focuses on relevant data sets, reveals that it is the AI/ML techniques that possess the upper hand over traditional methods when it comes to identifying anomalies and defending against threats for cloud- connected medical devices. Such finding expresses much significance for the healthcare industry, as it gives room for the inclusion of AI/ML techniques into the security systems of the medical devices, which are all connected to the cloud. Through the employment of these strategies, healthcare units will become better able to detect and halt any form of threat and as a consequence patients’ data will be protected, devices will continue operating effectively, and eventually patients’ safety and healthcare units will benefit and gain trust from patients.

Statement of Retraction: Light weight cryptography-based data hiding system for Internet of Medical Things

Statement of Retraction: Light weight cryptography-based data hiding system for Internet of Medical Things

RETRACTED ARTICLE: Light weight cryptography-based data hiding system for Internet of Medical Things

ABSTRACT Statement of Retraction: Light weight cryptography-based data hiding system for Internet of Medical Things We, the Editors and Publisher of the International Journal of Healthcare Management , have retracted the following article: Swetha Pesaru, Naresh K. Mallenahalli & B. Vishnu Vardhan (22 Dec 2022): Light weight cryptography-based data hiding system for Internet of Medical Things, International Journal of Healthcare Management, DOI: 10.1080/20479700.2022.2161145 Since publication, significant concerns have been raised about the integrity of the data and reported results in the article. When approached for an explanation, the authors did not provide their original data or any necessary supporting information. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article. The corresponding author listed in this publication has been informed. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

Associative tasks computing offloading scheme in Internet of medical things with deep reinforcement learning

Associative tasks computing offloading scheme in Internet of medical things with deep reinforcement learning

5G-backed resilience and quality enhancement in internet of medical things infrastructure for resilient infrastructure

5G-backed resilience and quality enhancement in internet of medical things infrastructure for resilient infrastructure

Enhancing Medical Data Analysis with Federated Learning in the Internet of Medical Things

The Internet of Things refers to physical items, which are equipped with software, sensors, computing power, and other technologies, and that communicate with other electronic devices and systems over communication networks or the Internet. A collection of medical devices and software programmes known as the Internet of Medical Things (IoMT) link to healthcare networks via internet computing. Machine-to-machine communication, which is the foundation of IoMT, is made feasible by medical equipment that includes Wi-Fi. IoMT devices have the ability to analyse and store collected data by connecting to cloud services. IoMT is a different moniker for IoT in healthcare. Since data is transferred via the internet and the IoMT creates a lot of data, privacy concerns are important. The vast volume of data produced by IoMT devices calls for big data processing, and federated learning tackles privacy issues as a way to overcome these difficulties. The big data health care framework for IoMT is discussed in this article. It is built on federated learning.

Determination of Security Factors Affecting Internet of Medical Things by Artificial Intelligence Technique

Determination of Security Factors Affecting Internet of Medical Things by Artificial Intelligence Technique

Strategic Assessment of Intricacies in Healthcare Cyber Security: Analyzing Distinctive Challenges, Evaluating Their Ramifications on Healthcare Delivery, and Proposing Advanced Mitigation Strategies

Healthcare firms have access to highly sensitive and valuable data, such as patient health records and payment card information. They are also becoming more reliant on Internet of Medical Things (IoMT) devices to provide treatment, and assaults on this networked equipment can result in data breaches or disruptions to crucial care of patients. Hence there is need to critically assess the intricacies in healthcare cyber security by analyzing the unique challenges facing healthcare sector, their impact on healthcare delivery and suggest some effective cyber security measures to mitigate the identified Cyber security challenges facing health care sector. The research employs a quantitative method by using the descriptive and survey approach through the use of questionnaires to elicit and gather relevant information regarding the Unique Cyber security Challenges in the Healthcare Industry. The descriptive method was used to achieve the objectives of the study while the survey technique was used to get qualitative information from respondents about effect of those cyber security challenges in the health care industry. The target sample size used for this study was 1300. However, only 980 responses were recovered and used for the analysis. Future research work suggested that strong encryption procedures can be developed and implemented for healthcare security, sophisticated anomaly detection tools can be incorporated, and cooperative frameworks for information exchange across the healthcare ecosystem could be established. Also the suggested effective measures to mitigate cyber security challenges in healthcare sector can be implemented in order to secure the patient sensitive and valuable data.

A Lightweight Identity-Based Network Coding Scheme for Internet of Medical Things

Network coding is a potent technique extensively utilized in decentralized Internet of Things (IoT) systems, including the Internet of Medical Things (IoMT). Nevertheless, the inherent packet-mixing characteristics of network coding expose data transmission to pollution attacks, potentially compromising the integrity of original files. The homomorphic signature scheme serves as a robust cryptographic tool that can bolster network coding’s resilience against such attacks. However, current schemes are computationally intensive for signature verification, making them impractical for IoMT environments. In this study, we propose a lightweight identity-based network coding scheme (IBNS) that minimizes computational overhead during the signing and verification processes. This scheme has been demonstrated to be secure against adaptive chosen-message attacks and is well-suited for IoMT applications. Furthermore, we assess the performance of our IBNS through both theoretical and experimental analyses. Simulation outcomes confirm that our scheme outperforms previous ones in terms of practicality and efficiency.

A Topical Review on Enabling Technologies for the Internet of Medical Things: Sensors, Devices, Platforms, and Applications.

The Internet of Things (IoT) is still a relatively new field of research, and its potential to be used in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasises IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualisation, and artificial intelligence in medical applications. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices has been carried out. After the year 2001, an integrated outcome of 986 articles was initially selected, and by applying the inclusion-exclusion criterion, a total of 597 articles were identified. 23 new studies have been finally found, including records from websites and citations. This review then analyses different sensor monitoring circuits in detail, considering an Intensive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented.

A Survey on Transforming Healthcare with IoMT : The Power of Connected Medical Devices

This thorough analysis examines current developments in medical technology, with an emphasis on the Internet of Medical Things (IoMT) and its uses. It covers IoMT's contribution in creating wearable internet monitors for dispersed monitoring systems, addressing pregnancy-related issues, and is divided into four subject groups. Along with blockchain adaption, it also highlights privacy and dependability in IoMT-based health monitoring systems. The paper also emphasizes the contributions of IoMT to healthcare diagnosis and monitoring, including machine learning and edge-AI-based cardiovascular health monitoring and fall prediction. It also covers data science and healthcare informatics techniques, such as using lightweight cryptography to ensure real-time security in remote patient monitoring and identifying unusual user behavior. IoMT has the potential to transform patient care, diagnostics, and data-driven decision-making across a range of healthcare areas, as the paper highlights overall.

Blockchain service platform evaluation with probabilistic linguistic preference information based on Choquet integral and ExpTODIM

The application of Internet of Medical Things (IoMT) can improve the treatment efficiency of medical institutions, but it still has hidden dangers in security and privacy. The introduction of blockchain technology into IoMT is expected to prevent these hidden dangers. This study proposes a multiple criteria decision aiding model to select a suitable blockchain service platform for IoMT to enhance its safety and reliability. The proposed model combines the probabilistic linguistic term set (PLTS), multiple criteria hierarchy process (MCHP), 2-additive Choquet integral and ExpTODIM method to assist decision-makers (DMs) to obtain a reliable and low-risk decision result. The MCHP is employed to analyze decision-making problems by hierarchical criteria. The 2-additive Choquet integral is adopted to reflect interactions between criteria, and parameter values in the 2-additive Choquet integral are indirectly determined based on the preference information of DMs. PLTSs are used to represent the evaluation information of experts and the preference information of DMs. The ExpTODIM (Exponential TODIM) method is adopted to rank alternatives considering the psychological characteristics of DMs. An illustrative example demonstrates the applicability and merits of the proposed model with comparative analysis.

Artificial Intelligence and Machine Learning Techniques for Anomaly Detection and Threat Mitigation in Cloud-Connected Medical Devices

The Internet of Medical Things (IoMT) has begun functioning like this: improved patient monitoring and an easily accessible digital data warehouse. Despite that, this methodology of the internet will potentially have a counter balance which risks for patient data might involve hacking, data theft, and unauthorized access that may contain great consequences for patient privacy and safety. This article examines the possibility of utilizing new AI technology, including inter alia deep learning, unsupervised learning, and ensemble learning to further boost anomaly detection and threat management in connected cloud medical systems. Many old rules and approaches based on statistics lose relevancy versus the dynamics and unpredictability of modern cyberattacks. Identification of anomalies in cyber security is nearly unavoidable, and it should be the first and the last reaction for detecting irregularities in behavior that may indicate undesirable acts or attacks. The paper aims at understanding how AI/ML approaches can give more sophisticated and versatile interventions for finding out anomalies in cloud-attached medical machines. Moreover, this research details robust AI/ML methods such as the adversarial machine learning and reinforcement learning for a perfect threat mitigation. These techniques which activates machine learning models to learn from data continuing to adjust to new evolving threats and then to establish intelligent and proactive threat response systems. The data experiment, which focuses on relevant data sets, reveals that it is the AI/ML techniques that possess the upper hand over traditional methods when it comes to identifying anomalies and defending against threats for cloud-connected medical devices. Such finding expresses much significance for the healthcare industry, as it gives room for the inclusion of AI/ML techniques into the security systems of the medical devices, which are all connected to the cloud. Through the employment of these strategies, healthcare units will become better able to detect and halt any form of threat and as a consequence patients’ data will be protected, devices will continue operating effectively, and eventually patients’ safety and healthcare units will benefit and gain trust from patients.

Advanced Heart Disease Prediction: Deep Learning-Enhanced Convolutional Neural Network in the Internet of Medical Things Environment

Using the large dataset from the UCI Machine Learning Repository, this study presents a state-of-the-art Hybrid Convolutional Neural Network (HCNN) for predicting heart disease. The HCNN design has convolutional layers, leftover blocks, and attention methods. These help pull more features from cardiovascular health data, even when the patterns are very complex. Utilizing these deep learning parts, the HCNN shows better prediction skills, getting higher accuracy, strong classification (AUC), and a fair F1 Score. Because the model can change to find complicated connections in the information, it could be used to make medical diagnosis better. The HCNN is unique because it can instantly learn hierarchical structures from raw data. This lets it find hidden features that are important for accurately predicting heart disease. The convolutional layers help the model find local patterns, and the residue blocks stop problems with disappearing gradients, which makes training for deep designs more efficient. Attention processes make the network even more focused on important traits, which adds to its amazing ability to tell them apart. This work opens the door for researchers, doctors, and data scientists to use deep learning, and especially HCNN, to improve cardiovascular health analytics. This study takes a big step toward more accurate and efficient heart disease prognosis by giving a complete overview of the model's architecture and performance metrics. This opens the door for more research and use of advanced neural networks in the field of predictive medical diagnostics.

Collaboration of Vague Theory and Mathematical Techniques for Optimizing Healthcare by Recommending Optimal Blockchain Supplier

The Internet of Medical Things (IoMTs) has the potential to revolutionize healthcare delivery by connecting medical devices and applications to healthcare IT systems via the internet. This connectivity enables the collection, transmission, and analysis of patient data, facilitating remote healthcare delivery and enhancing patient outcomes. However, the security and privacy of the data transmitted and stored within IoMT systems remain critical concerns. Blockchain technology offers a promising solution to address these challenges in IoMTs applications. Blockchain provides a decentralized and immutable ledger where transactions, in this case, patient data, are securely recorded and cannot be altered retroactively. This ensures the integrity and confidentiality of sensitive medical information while eliminating the need for a central authority to manage data exchange. This study proposes a comprehensive fuzzy decision-making framework for selecting the best blockchain provider in the IoMT field. The framework comprises two main steps: Determining the relative importance weights of the criteria utilizing the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Determining the most suitable supplier for blockchain services providers employing the Ranking Alternatives with the Fuzzy ROOT Assessment Method (RAM) technique. A comparison between Fuzzy Multi-Criteria Decision-Making (MCDM) methods is employed to ensure the robustness and validity of the proposed framework.

Proxy Re-Encryption for Secure Data Sharing with Blockchain in Internet of Medical Things

The Internet of Medical Things (IoMT) is crucial in our daily health monitoring and assisting doctors in treatment. However, the existing schemes mainly use cloud servers to solve the storage of large amounts of data generated by smart devices, ignoring the guarantee mechanism of ciphertext integrity, which may result in data being tampered with or replaced in data sharing. To address this issue, we propose a secure data sharing scheme named Proxy Re-Encryption for Secure Data Sharing with Blockchain in Internet of Medical Things (PRE-IoMT). Specifically, we propose a new proxy re-encryption data sharing approach by introducing identity hash during the key generation phase to realize the binding of public key and user identity, thus enhancing the security of data sharing in PRE-IoMT. Moreover, we adopt BlockChain transactions to design a pairing function ciphertext check mechanism, which can effectively prevent the ciphertext stored in the cloud server from being tampered with. We add the user to the accumulator by invoking the BlockChain smart contract to realize the management and fast query of users. Finally, we conduct the performance analysis experiments on the four-stage computation of proxy re-encryption, communication overhead, storage overhead, transmission delay, and the delay of BlockChain transactions, compared with existing schemes, our scheme improves the computation efficiency of encryption, re-encryption, decryption, and re-decryption by about 23.8%, 71.4%, 48%, and 15.3% respectively. The security proof also exhibits that PRE-IoMT can resist IND-ID-CPA.

Edge AI for Internet of Medical Things: A literature review

The Internet of Things (IoT) consists of heterogeneous devices such as wearables and monitoring devices that collect data to provide autonomous decision-making and smart applications. IoT technologies, such as the Internet of Medical Things (IoMT), have become gradually popular for medical purposes, combining IoT and medical devices to achieve good health and well-being. However, IoMT devices are often tight and have resource constraints, which leads to limited local data processing in the device. Edge computing provides access to additional computation and storage resources for IoMT devices, bringing intelligent processing closer to the data sources. This technology opens up great possibilities for IoMT applications, especially when combined with Artificial Intelligence (AI). Edge AI runs AI computations close to the IoT devices and users instead of centralized services such as cloud servers. This paper investigates the potential of Edge AI and IoMT. In this sense, this survey is the first work to further detail Edge AI and Machine Learning Operations in IoMT domains and wearable technology, thus contributing to the literature by comprehensively exploring the potential of ML strategies and operations at the network’s edge and intelligence distribution. This study also presents a case study on heart anomaly detection.

Privacy and convergence analysis for the internet of medical things using massive MIMO

Machine learning is the analysis based on data that gives strategic decisions to cultivate an accurate and stable framework for different applications. Access to medical data with the utmost privacy and high data rates is still a challenging problem. To accomplish the above-mentioned features, the performance of federated learning (FL) with 5G massive multiple-input-multiple-output (MIMO) is investigated for IoMT systems. This provides an energy-efficient and privacy-preserving solution with high throughput for digital health system. In the proposed model, the uplink scenario is investigated using different detection techniques. The performance are evaluated at the central server and edge devices for different signal-to-noise ratios (SNRs) and different fading channels. The ML bit error rate (BER) is better than MRC but with higher complexity. The accuracy obtained is approximately 90% with an improvement of around 8% to 9% as compared to the baseline approach.

Optimized data management and secured federated learning in the Internet of Medical Things (IoMT) with blockchain technology

The increasing need for improved medical and healthcare monitoring systems is paramount in the current healthcare environment. This need is propelled by the continuously growing quantity of patient data, the imperative for instantaneous information, and the crucial significance of data security. The Internet of Medical Things (IoMT) has been recognized as an essential technology for addressing healthcare requirements via the seamless integration of medical devices, sensors, and healthcare systems. Nevertheless, the effective administration and safeguarding of the extensive and delicate data produced by the IoMT present significant obstacles. The current methodologies often need to be revised to deliver holistic resolutions to these difficulties. To tackle these concerns comprehensively, the research proposed Optimized Data Management and Secured Federated Learning (ODMSM-FL), incorporating blockchain provenance. ODMSM-FL has been specifically developed to address the unique requirements of the IoMT via several capabilities, including decentralized data administration, federated learning, and solid security mechanisms based on blockchain technology. The system enhances data management capabilities, facilitating the adequate storage, retrieval, and exchange of medical data while safeguarding data privacy and maintaining data integrity. This study reports a set of numerical findings about essential performance metrics: transaction throughput (102.75 Kbps), block processing time (42.28 ms), data retrieval latency (64.02 ms), convergence rate (77.53 %), security (88.97 %), and accuracy (86.32 %). The findings prove that ODMSM-FL surpasses current methodologies, showcasing its exceptional capacity to improve data management, security, and overall performance within IoMT applications. The findings underscore the considerable potential of ODMSM-FL in effectively tackling the urgent data management and security issues in the IoMT, eventually making valuable contributions to the progress of healthcare systems and the enhanced welfare of patients.

Extensive Review of Literature on Explainable AI (XAI) in Healthcare Applications

Abstract: Artificial Intelligence (AI) techniques are widely being used in the medical fields or various applications including diagnosis of diseases, prediction and classification of diseases, drug discovery, etc. However, these AI techniques are lacking in the transparency of the predictions or decisions made due to their black box-type operations. The explainable AI (XAI) addresses such issues faced by AI to make better interpretations or decisions by physicians. This article explores XAI techniques in the field of healthcare applications, including the Internet of Medical Things (IoMT). XAI aims to provide transparency, accountability, and traceability in AI-based systems in healthcare applications. It can help in interpreting the predictions or decisions made in medical diagnosis systems, medical decision support systems, smart wearable healthcare devices, etc. Nowadays, XAI methods have been utilized in numerous medical applications over the Internet of Things (IOT), such as medical diagnosis, prognosis, and explanations of the AI models, and hence, XAI in the context of IoMT and healthcare has the potential to enhance the reliability and trustworthiness of AI systems.