A Secured Patient’s Online Data Monitoring through Blockchain: An Intelligent way to Store Lifetime Medical Records

Hossain, Md. Jobaer; Wadud, Md. Anwar Hussen; Rahman, Asheq; Ferdous, Jannatul; Alam, Md. Shahin; Bhuiyan, T. M. Amir - Ul - Haque; Mridha, M. F.

doi:10.1109/icsct53883.2021.9642537

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…With the advent of new variant of coronavirus, medical personnel are facing an enormous challenge to address the health issues of the infected individual. The complexities of COVID-19 coupled with scarce resources and rising healthcare cost has encouraged many to access remote health management services using computer or similar devices [137][138][139][140]. IoT plays a fundamental act in achieving automated distant health care services.…”

Section: E-healthcare Systemmentioning

confidence: 99%

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

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Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

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Section: E-healthcare Systemmentioning

confidence: 99%

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

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“…In the following, we describe some of the most common BC applications. For the sake of completeness, other interesting ones are reported in Table 3 where Application field [115] 2021 Application of BC for the sustainability of Prefabricated Housing Construction [116] 2021 Barriers for adopting BC technology in sustainable supply chains [117] 2021 Constructing a smart city IoT framework with BC and SDN that is energy aware and distributed securely [118] 2021 Blockchain application for supply chain in the area of construction industry [119] 2021 Blokchain technology for the safety of the management of food data [120] 2021 Application of Blockchain for financial sector [121] 2021 BC-based architecture for intelligent vaccine distribution approaches in COVID-19 pandemic situation [122] 2021 Integration of Blockchain for Supply chain management [123] 2021 Blockchain-based privacy for smart healthcare management [124] 2020 Impact of BC on applications covering different industrial sectors [125] 2020 Effects of BC technology on the logistics industry and other business models [126] 2020 Framework for the analysis of BC integrating institutional, market, and technical factors [127] 2020 BC-based decentralized app for smart building system management [128] 2020 Blockchain-based security for cloud storage management in IoT networks [129] 2019 Applications in the architecture, engineering, and construction industries [130] 2019 BC-enabled SCs in different application scenarios (e.g., finance, energy, etc.) [131] 2019 Review of the BC systems exploited in the oil and gas industry [132] 2019 Main trends of BC usage in supply chains management and logistics [133] 2019 SDN-IoT model with NFV implementation for smart cities based on a distributed protected BC architecture [134] 2019 Applications for smart communities (e.g., smart grid, transportation, healthcare) [135] 2018 BC-connected gateways to maintain user privacy in IoT networks [136] 2018 LedgerGuard: a tool for ledger integrity, detecting and recovering corrupted blocks [137] 2018 Patient monitoring system using SC and private BC based on the Ethereum protocol Table 3 (continued)…”

Section: Blockchain Applicationsmentioning

confidence: 99%

On the Integration of Blockchain and SDN: Overview, Applications, and Future Perspectives

et al. 2022

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Blockchain (BC) and software-defined networking (SDN) are leading technologies which have recently found applications in several network-related scenarios and have consequently experienced a growing interest in the research community. Indeed, current networks connect a massive number of objects over the Internet and in this complex scenario, to ensure security, privacy, confidentiality, and programmability, the utilization of BC and SDN have been successfully proposed. In this work, we provide a comprehensive survey regarding these two recent research trends and review the related state-of-the-art literature. We first describe the main features of each technology and discuss their most common and used variants. Furthermore, we envision the integration of such technologies to jointly take advantage of these latter efficiently. Indeed, we consider their group-wise utilization—named BC–SDN—based on the need for stronger security and privacy. Additionally, we cover the application fields of these technologies both individually and combined. Finally, we discuss the open issues of reviewed research and describe potential directions for future avenues regarding the integration of BC and SDN. To summarize, the contribution of the present survey spans from an overview of the literature background on BC and SDN to the discussion of the benefits and limitations of BC–SDN integration in different fields, which also raises open challenges and possible future avenues examined herein. To the best of our knowledge, compared to existing surveys, this is the first work that analyzes the aforementioned aspects in light of a broad BC–SDN integration, with a specific focus on security and privacy issues in actual utilization scenarios.

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“…• Genomic analysis: The process of studying and examining the different gene sequencing and molecular biology to identify the inherited gene disorders to support medical care decisions (Atta-Ur-Rahman et al, 2022). • Privacy and fraud analysis: Reducing fraud by assessing and analysing the massive set of claim data; velocity of claim requests handled is an issue (Hossain et al, 2021, Haque & Tozal, 2022). • Disease surveillance or device monitoring: Information-based activity involving capturing and analysing large real-time data originating from different origins, e.g., in-hospital and in-home devices for unfavourable event predictions (Taimoor & Rehman, 2022).…”

Section: Applications Of Big Data In Healthcarementioning

confidence: 99%

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

Shetty¹,

Ananthanarayana²,

Mahale³

2022

AIP

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Over the past few decades, the enormous expansion of medical data has led to searching for ways of data analysis in smart healthcare systems. Acquisition of data from pictures, archives, communication systems, electronic health records, online documents, radiology reports and clinical records of different styles with specific numerical information has given rise to the concept of multimodality and the need for machine learning and deep learning techniques in the analysis of the healthcare system. Medical data play a vital role in medical education and diagnosis; determining dependency between distinct modalities is essential. This paper gives a gist of current radiology medical data analysis techniques and their various approaches and frameworks for representation and classification. A brief outline of the existing medical multimodal data processing work is presented. The main objective of this study is to spot gaps in the surveyed area and list future tasks and challenges in radiology. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (or PRISMA) guidelines were incorporated in this study for effective article search and to investigate several relevant scientific publications. The systematic review was carried out on multimodal medical data analysis and highlighted advantages, limitations and strategies. The inherent benefit of multimodality in the medical domain powered with artificial intelligence has a significant impact on the performance of the disease diagnosis frameworks.

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A Secured Patient’s Online Data Monitoring through Blockchain: An Intelligent way to Store Lifetime Medical Records

Cited by 12 publications

References 26 publications

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

On the Integration of Blockchain and SDN: Overview, Applications, and Future Perspectives

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

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