Integrating blockchain technology with artificial intelligence for cardiovascular medicine

Krittanawong, Chayakrit; Rogers, Albert J.; Aydar, Mehmet; Choi, Edward; Johnson, Kipp W.; Wang, Zhen; Narayan, Sanjiv M.

doi:10.1038/s41569-019-0294-y

Cited by 96 publications

(165 citation statements)

References 7 publications

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“…The technical shift to consumer-driven technology might provide a catalyst to standardize biosensor and data formats, and in turn increase security. Blockchain technology, which has been successfully used in financial markets and other industries, might have a role in patient-centred monitoring by tagging data ownership, providing traceability and enabling incentive programmes for sharing data 83 .…”

Section: Data Securitymentioning

confidence: 99%

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

et al. 2020

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Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. T he se tools are increasingly recognized by regulatory a g e n c ies a n d p r o f e s s ional m e d ic al s oc ie ti es, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.

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Section: Data Securitymentioning

confidence: 99%

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

et al. 2020

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“…In a recent review by Krittanawong et al. [ 33 ] summarizes the potential opportunities and challenges by integrating blockchain with AI to develop personalized medicine for cardiovascular, according to their review this combination can work as a booster for reliable data availability needed for personalized medicines, but still, it is needed to consider the privacy of patients and data producers. Debe et al.…”

Section: Related Workmentioning

confidence: 99%

Edge Computing to Secure IoT Data Ownership and Trade with the Ethereum Blockchain

Nawaz

Queralta

Guan

et al. 2020

Sensors

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With an increasing penetration of ubiquitous connectivity, the amount of data describing the actions of end-users has been increasing dramatically, both within the domain of the Internet of Things (IoT) and other smart devices. This has led to more awareness of users in terms of protecting personal data. Within the IoT, there is a growing number of peer-to-peer (P2P) transactions, increasing the exposure to security vulnerabilities, and the risk of cyberattacks. Blockchain technology has been explored as middleware in P2P transactions, but existing solutions have mainly focused on providing a safe environment for data trade without considering potential changes in interaction topologies. we present EdgeBoT, a proof-of-concept smart contracts based platform for the IoT built on top of the ethereum blockchain. With the Blockchain of Things (BoT) at the edge of the network, EdgeBoT enables a wider variety of interaction topologies between nodes in the network and external services while guaranteeing ownership of data and end users’ privacy. in EdgeBoT, edge devices trade their data directly with third parties and without the need of intermediaries. This opens the door to new interaction modalities, in which data producers at the edge grant access to batches of their data to different third parties. Leveraging the immutability properties of blockchains, together with the distributed nature of smart contracts, data owners can audit and are aware of all transactions that have occurred with their data. we report initial results demonstrating the potential of EdgeBoT within the IoT. we show that integrating our solutions on top of existing IoT systems has a relatively small footprint in terms of computational resource usage, but a significant impact on the protection of data ownership and management of data trade.

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“…It is achieved by establishing a set of time‐stamped blocks linked by a cryptographic hash. Blockchain has been widely accepted as an effective solution for trust and security issues due to its potential for information transparency and tamper prevention (Andoni et al., 2019; Krittanawong et al., 2020). Aside from the typical application of blockchain in cryptocurrencies, a number of interesting uses are unfolding, including end‐to‐end verifiable electronic voting, healthcare record management, identity management systems, access control systems, decentralized notaries, supply chain management, and food traceability (Di Francesco Maesa & Mori, 2020).…”

Section: Blockchain and Food Traceability Systemmentioning

confidence: 99%

Filling the trust gap of food safety in food trade between the EU and China: An interconnected conceptual traceability framework based on blockchain

Qian

et al. 2020

Food and Energy Security

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Integrating blockchain technology with artificial intelligence for cardiovascular medicine

Cited by 96 publications

References 7 publications

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

Edge Computing to Secure IoT Data Ownership and Trade with the Ethereum Blockchain

Filling the trust gap of food safety in food trade between the EU and China: An interconnected conceptual traceability framework based on blockchain

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