86 research outputs found

    Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

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    With the advent of the IoT, AI, ML, and DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in \ac{FL} have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable \ac{FL} models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of \ac{FL}, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.Comment: Accepted at IEEE Internet of Things Journa

    A systematic review of blockchain in healthcare : frameworks, prototypes, and implementations

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    Blockchain, a form of distributed ledger technology has attracted the interests of stakeholders across several sectors including healthcare. Its' potential in the multi-stakeholder operated sector like health has been responsible for several investments, studies, and implementations. Electronic Health Records (EHR) systems traditionally used for the exchange of health information amongst healthcare stakeholders have been criticised for centralising power, failures and attack-points with exchange data custodians. EHRs have struggled in the face of multi-stakeholder and system requirements while adhering to security, privacy, ethical and other regulatory constraints. Blockchain is promising amongst others to address the many EHR challenges, primarily trustless and secure exchange of health information amongst stakeholders. Many blockchain-in-healthcare frameworks have been proposed; some prototyped and/or implemented. This study leveraged the PRISMA framework to systematically search and evaluate the different models proposed; prototyped and/or implemented. The bibliometric and functional distribution of all 143 articles from this study were presented. This study evaluated 61 articles that discussed either prototypes or pilot or implementations. The technical and architectural analysis of these 61 articles for privacy, security, cost, and performance were detailed. Blockchain was found to solve the trust, security and privacy constraints of traditional EHRs often at significant performance, storage and cost trade-offs.peer-reviewe

    Analyzing the Prospects of Blockchain in Healthcare Industry

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    Deployment of a secured healthcare information is a major challenge in a web based environment. Ehealth services are subjected to same security threats as other services. The purpose of blockchain is to provide a structure and security to the organization data. Healthcare data deals with confidential information. The medical records can be well organized and empower their propagation in a secured manner through the usage of blockchain technology. The study throws light on providing security of health services through blockchain technology. The authors have analysed the various aspects of role of blockchain in healthcare through an extensive literature review. The application of blockchain in covid-19 has also been analysed and discussed in the study. Further application of blockchain in Indian healthcare has been highlighted in the paper. The study provides suggestions for strengthening the healthcare system by blending machine learning, artificial intelligence, big data, IoT with blockchain

    A Survey on Blockchain-Based IoMT Systems: Towards Scalability

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    peer reviewedRecently, blockchain-based Internet of Medical Things (IoMT) has started to receive more attention in the healthcare domain as it not only improves the care quality using real-time and continuous monitoring but also minimizes the cost of care. However, there is a clear trend to include many entities in IoMT systems, such as IoMT sensor nodes, IoT wearable medical devices, patients, healthcare centers, and insurance companies. This makes it challenging to design a blockchain framework for these systems where scalability is a most critical factor in blockchain technology. Motivated by this observation, in this survey we review the state-of-the-art in blockchain-IoMT systems. Comparison and analysis of such systems prove that there is a substantial gap, which is the negligence of scalability. In this survey, we discuss several approaches proposed in the literature to improve the scalability of blockchain technology, and thus overcoming the above mentioned research gap. These approaches include on-chain and off-chain techniques, based on which we give recommendations and directions to facilitate designing a scalable blockchain-based IoMT system. We also recommended that a designer considers the well-known trilemma along with the various dimensions of a scalable blockchain system to prevent sacrificing security and decentralization as well. Moreover, we raise several research questions regarding benchmarking; addressing these questions could help designers determining the existing bottlenecks, leading to a scalable blockchain

    Design revolutions: IASDR 2019 Conference Proceedings. Volume 4: Learning, Technology, Thinking

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    In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 4 includes papers from Learning, Technology and Thinking tracks of the conference

    Validation of design artefacts for blockchain-enabled precision healthcare as a service.

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    Healthcare systems around the globe are currently experiencing a rapid wave of digital disruption. Current research in applying emerging technologies such as Big Data (BD), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Augmented Reality (AR), Virtual Reality (VR), Digital Twin (DT), Wearable Sensor (WS), Blockchain (BC) and Smart Contracts (SC) in contact tracing, tracking, drug discovery, care support and delivery, vaccine distribution, management, and delivery. These disruptive innovations have made it feasible for the healthcare industry to provide personalised digital health solutions and services to the people and ensure sustainability in healthcare. Precision Healthcare (PHC) is a new inclusion in digital healthcare that can support personalised needs. It focuses on supporting and providing precise healthcare delivery. Despite such potential, recent studies show that PHC is ineffectual due to the lower patient adoption in the system. Anecdotal evidence shows that people are refraining from adopting PHC due to distrust. This thesis presents a BC-enabled PHC ecosystem that addresses ongoing issues and challenges regarding low opt-in. The designed ecosystem also incorporates emerging information technologies that are potential to address the need for user-centricity, data privacy and security, accountability, transparency, interoperability, and scalability for a sustainable PHC ecosystem. The research adopts Soft System Methodology (SSM) to construct and validate the design artefact and sub-artefacts of the proposed PHC ecosystem that addresses the low opt-in problem. Following a comprehensive view of the scholarly literature, which resulted in a draft set of design principles and rules, eighteen design refinement interviews were conducted to develop the artefact and sub-artefacts for design specifications. The artefact and sub-artefacts were validated through a design validation workshop, where the designed ecosystem was presented to a Delphi panel of twenty-two health industry actors. The key research finding was that there is a need for data-driven, secure, transparent, scalable, individualised healthcare services to achieve sustainability in healthcare. It includes explainable AI, data standards for biosensor devices, affordable BC solutions for storage, privacy and security policy, interoperability, and usercentricity, which prompts further research and industry application. The proposed ecosystem is potentially effective in growing trust, influencing patients in active engagement with real-world implementation, and contributing to sustainability in healthcare

    “Be a Pattern for the World”: The Development of a Dark Patterns Detection Tool to Prevent Online User Loss

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    Dark Patterns are designed to trick users into sharing more information or spending more money than they had intended to do, by configuring online interactions to confuse or add pressure to the users. They are highly varied in their form, and are therefore difficult to classify and detect. Therefore, this research is designed to develop a framework for the automated detection of potential instances of web-based dark patterns, and from there to develop a software tool that will provide a highly useful defensive tool that helps detect and highlight these patterns

    2019-2020 Lindenwood University Graduate Course Catalog

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    Lindenwood University Graduate Course Cataloghttps://digitalcommons.lindenwood.edu/catalogs/1187/thumbnail.jp

    Technical Debt is an Ethical Issue

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    We introduce the problem of technical debt, with particular focus on critical infrastructure, and put forward our view that this is a digital ethics issue. We propose that the software engineering process must adapt its current notion of technical debt – focusing on technical costs – to include the potential cost to society if the technical debt is not addressed, and the cost of analysing, modelling and understanding this ethical debt. Finally, we provide an overview of the development of educational material – based on a collection of technical debt case studies - in order to teach about technical debt and its ethical implication
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