Blockchain Papers

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236 papersLast indexed Aug 31, 2026
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Oct 22, 2020¡Research Square
12 cites
Research on intelligent medical big data system based on Hadoop and blockchain

Xiangfeng Zhang, Yanmei Wang

Abstract In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data Distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

Open access
Artificial Intelligence in Healthcare
Original source
Sep 19, 2020¡International Journal of Medical Informatics
277 cites
The role of blockchain technology in telehealth and telemedicine

Raja Wasim Ahmad, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob ¡ 6 authors

<div><b>Objectives: </b>Telehealth and telemedicine systems aim to deliver remote healthcare services to mitigate the spread of COVID‐19. Also, they can help to manage scarce healthcare resources to control the massive burden of COVID-19 patients in hospitals. However, a large portion of today's telehealth and telemedicine systems are centralized and fall short of providing necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials.</div><div><b>Methods: </b>The current study has explored the potential opportunities and adaptability challenges for blockchain technology in telehealth and telemedicine sector. It has explored the key role that blockchain technology can play to provide necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials.</div><div><b>Results: </b>Blockchain technology can improve telehealth and telemedicine services by offering remote healthcare services in a manner that is decentralized, tamper-proof, transparent, traceable, reliable, trustful, and secure. It enables health professionals to accurately identify frauds related to physician educational credentials and medical testing kits commonly used for home-based diagnosis.</div><div><b>Conclusions: </b>Wide deployment of blockchain in telehealth and telemedicine technology is still in its infancy. Several challenges and research problems need to be resolved to enable the widespread adoption of blockchain technology in telehealth and telemedicine systems.</div><div> </div><div><br></div>

Open access
4 source records
Blockchain Technology Applications and Security
Organizational and Employee Performance
Internet of Things and AI
Original source
Aug 26, 2020¡International Journal of Interactive Multimedia and Artificial Intelligence
70 cites
Blockchain for Healthcare: Securing Patient Data and Enabling Trusted Artificial Intelligence.

H. S. Jennath, V. S. Anoop, S. Asharaf

Advances in information technology are digitizing the healthcare domain with the aim of improved medical services, diagnostics, continuous monitoring using wearables, etc., at reduced costs. This digitization improves the ease of computation, storage and access of medical records which enables better treatment experiences for patients. However, it comes with a risk of cyber attacks and security and privacy concerns on this digital data. In this work, we propose a Blockchain based solution for healthcare records to address the security and privacy concerns which are currently not present in existing e-Health systems. This work also explores the potential of building trusted Artificial Intelligence models over Blockchain in e-Health, where a transparent platform for consent-based data sharing is designed. Provenance of the consent of individuals and traceability of data sources used for building and training the AI model is captured in an immutable distributed data store. The audit trail of the data access captured using Blockchain provides the data owner to understand the exposure of the data. It also helps the user to understand the revenue models that could be built on top of this framework for commercial data sharing to build trusted AI models.

Open access
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Original source
Aug 18, 2020¡IEEE Transactions on Industrial Informatics
482 cites
Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks

Yunlong Lu, Xiaohong Huang, Ke Zhang, Sabita Maharjan ¡ 5 authors

Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods.

Open access
2 source records
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
Blockchain Technology Applications and Security
Original source
Jul 15, 2020¡Arabian Journal for Science and Engineering
104 cites
Applications of Blockchain Technology in Clinical Trials: Review and Open Challenges

Ilhaam A. Omar, Raja Jayaraman, Khaled Salah, Ibrar Yaqoob ¡ 5 authors

Blockchain technology has disclosed unprecedented opportunities in the healthcare sector by unlocking the true value of interoperability. Specifically, the striking features of blockchain technology, such as data provenance, transparency, decentralized transaction validation, and immutability can help to compensate for stringent data management issues (e.g., patient recruitment, persistent monitoring, data management, and data analytics and accurate reporting) in clinical trials (CTs). Although several research studies show that blockchain solutions help to improve patient retention, data integrity, privacy, and ensure CTs compliance with regulatory policies, a comprehensive survey on this topic is lacking. In this survey, we provide insights into the adoption of blockchain technology in CTs. We categorize and classify the literature by devising a meticulous taxonomy of the decentralized tasks of CT and practices based on indispensable parameters. Furthermore, we provide insights on works in progress towards deploying blockchain solutions in CTs. Finally, we identify and discuss several challenges that hinder the successful implementation of blockchain technologies in CTs.

Open access
2 source records
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Artificial Intelligence in Healthcare and Education
Original source
Jul 10, 2020¡arXiv (Cornell University)
487 cites
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging

Rajesh Kumar, Abdullah Aman Khan, Zhang, Sinmin, Jay Kumar ¡ 10 authors

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients' data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.

Open access
3 source records
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Privacy-Preserving Technologies in Data
Original source
May 26, 2020¡JMIR Publications Inc.
4 cites
Blockchain Applications in Health Care and Public Health: Increased Transparency (Preprint)

Pedro Elkind Velmovitsky, Frederico M. Bublitz, Laura Fadrique, Plinio Pelegrini Morita

BACKGROUND Although big data and smart technologies allow for the development of precision medicine and predictive models in health care, there are still several challenges that need to be addressed before the full potential of these data can be realized (eg, data sharing and interoperability issues, lack of massive genomic data sets, data ownership, and security and privacy of health data). Health companies are exploring the use of blockchain, a tamperproof and distributed digital ledger, to address some of these challenges. OBJECTIVE In this viewpoint, we aim to obtain an overview of blockchain solutions that aim to solve challenges in health care from an industry perspective, focusing on solutions developed by health and technology companies. METHODS We conducted a literature review following the protocol defined by Levac et al to analyze the findings in a systematic manner. In addition to traditional databases such as IEEE and PubMed, we included search and news outlets such as CoinDesk, CoinTelegraph, and Medium. RESULTS Health care companies are using blockchain to improve challenges in five key areas. For electronic health records, blockchain can help to mitigate interoperability and data sharing in the industry by creating an overarching mechanism to link disparate personal records and can stimulate data sharing by connecting owners and buyers directly. For the drug (and food) supply chain, blockchain can provide an auditable log of a product’s provenance and transportation (including information on the conditions in which the product was transported), increasing transparency and eliminating counterfeit products in the supply chain. For health insurance, blockchain can facilitate the claims management process and help users to calculate medical and pharmaceutical benefits. For genomics, by connecting data buyers and owners directly, blockchain can offer a secure and auditable way of sharing genomic data, increasing their availability. For consent management, as all participants in a blockchain network view an immutable version of the truth, blockchain can provide an immutable and timestamped log of consent, increasing transparency in the consent management process. CONCLUSIONS Blockchain technology can improve several challenges faced by the health care industry. However, companies must evaluate how the features of blockchain can affect their systems (eg, the append-only nature of blockchain limits the deletion of data stored in the network, and distributed systems, although more secure, are less efficient). Although these trade-offs need to be considered when viewing blockchain solutions, the technology has the potential to optimize processes, minimize inefficiencies, and increase trust in all contexts covered in this viewpoint. CLINICALTRIAL

Open access
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Data-Driven Disease Surveillance
Original source
May 26, 2020¡Electronics
77 cites
Improving the Healthcare Effectiveness: The Possible Role of EHR, IoMT and Blockchain

Francesco Girardi, Gaetano De Gennaro, Lucio Colizzi, Vito Nicola Convertini

New types of patient health records aim to help physicians shift from a medical practice, often based on their personal experience, towards one of evidence based medicine, thus improving the communication among patients and care providers and increasing the availability of personal medical information. These new records, allowing patients and care providers to share medical data and clinical information, and access them whenever they need, can be considered enabling Ambient Assisted Living technologies. Furthermore, new personal disease monitoring tools support specialists in their tasks, as an example allowing acquisition, transmission and analysis of medical images. The growing interest around these new technologies poses serious questions regarding data integrity and transaction security. The huge amount of sensitive data stored in these new records surely attracts the interest of malicious hackers, therefore it is necessary to guarantee the integrity and the maximum security of servers and transactions. Blockchain technology can be an important turning point in the development of personal health records. This paper discusses some issues regarding the management and protection of health data exchanged through new medical or diagnostic devices.

Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Artificial Intelligence in Healthcare and Education
Original source
Apr 14, 2020¡IEEE Access
220 cites
Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Open access
5 source records
Blockchain Technology Applications and Security
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Original source
Jan 1, 2020¡IEEE Access
87 cites
Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM)

Fadila Zerka, Visara Urovi, Akshayaa Vaidyanathan, Samir Barakat ¡ 11 authors

The utility of Artificial Intelligence (AI) in healthcare strongly depends upon the quality of the data used to build models, and the confidence in the predictions they generate. Access to sufficient amounts of high-quality data to build accurate and reliable models remains problematic owing to substantive legal and ethical constraints in making clinically relevant research data available offsite. New technologies such as distributed learning offer a pathway forward, but unfortunately tend to suffer from a lack of transparency, which undermines trust in what data are used for the analysis. To address such issues, we hypothesized that, a novel distributed learning that combines sequential distributed learning with a blockchain-based platform, namely Chained Distributed Machine learning C-DistriM, would be feasible and would give a similar result as a standard centralized approach. C-DistriM enables health centers to dynamically participate in training distributed learning models. We demonstrate C-DistriM using the NSCLC-Radiomics open data to predict two-year lung-cancer survival. A comparison of the performance of this distributed solution, evaluated in six different scenarios, and the centralized approach, showed no statistically significant difference (AUCs between central and distributed models), all DeLong tests yielded p -val >0.05. This methodology removes the need to blindly trust the computation in one specific server on a distributed learning network. This fusion of blockchain and distributed learning serves as a proof-of-concept to increase transparency, trust, and ultimately accelerate the adoption of AI in multicentric studies. We conclude that our blockchain-based model for sequential training on distributed datasets is a feasible approach, provides equivalent performance to the centralized approach.

Open access
Radiomics and Machine Learning in Medical Imaging
Advanced X-ray and CT Imaging
Artificial Intelligence in Healthcare and Education
Original source
Jan 1, 2020¡IEEE Access
292 cites
Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

Md. Abdur Rahman, M. Shamim Hossain, Mohammad Saiful Islam, Nabil Alrajeh ¡ 5 authors

Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner's premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.

Open access
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Original source
Dec 10, 2019¡Frontiers in Blockchain
31 cites
From Clinical Trials to Highly Trustable Clinical Trials: Blockchain in Clinical Trials, a Game Changer for Improving Transparency?

Mehdi Benchoufi, Doug Altman, Philippe Ravaud

Quality of clinical research is undermined by severe misconducts, errors, frauds, which are detrimental to the trust it should arouse. In this perspective article, we show how Blockchain may trace and control processes of Clinical Trials preventing from the above issues or at least discourage them since they would become traceable and opposable. Then, we propose a short and doable program where, amidst the complex stream of events that a Clinical Trials consist of, we select sensitive and misconduct-prone steps that could dramatically benefit from Blockchain, through either simple core features as traceability and incorruptibility of its data registration, or through more refined automation tools called Smart Contracts.

Open access
Blockchain Technology Applications and Security
Ethics in Clinical Research
Artificial Intelligence in Healthcare and Education
Original source
Jul 4, 2019¡Blockchain in Healthcare Today
23 cites
Implementation Considerations for Blockchain in Healthcare Institutions

Ketan Paranjape, Mitchell Parker, David Houlding, Josip Car

Objective: This article aims to provide a primer on blockchain technology and implementation considerations for blockchain at healthcare institutions.  Results: After research and interviews, we developed a primer and a high-level implementation guide for healthcare systems exploring the use of blockchain technology.  Conclusions: The use of blockchain technology in health care is at a promising stage in development but blockchain-based applications are yet to be demonstrated as a viable platform for exchanging and reviewing information. Healthcare systems should be cautiously optimistic regarding the potential of blockchain and do a thorough business and technical diligence that is driven by targeted use cases to be successful. 

Open access
Healthcare cost, quality, practices
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Original source
May 1, 2019¡Revue d intelligence artificielle
26 cites
Avoiding Interoperability and Delay in Healthcare Monitoring System Using Block Chain Technology

V. Lakshman Narayana, Arepalli Peda Gopi, Kosaraju Chaitanya

Blockchain is using in every aspect now because of its distributed ledger which is immutable. It provides the information to the users directly without any third party involvement. It mediates the transactions directly between the interacting parties securely. It also eliminates the friction and also the cost of current intermediaries. It is now using in healthcare system to provide the interoperability, security, decentralization and other. EMR is presently using in healthcare which has some issues. The issues in healthcare are patient cannot access the data of his/her own health information. So by this healthcare has issues like interoperability and delay in communication and some other. These issues can be solved by using the Blockchain in healthcare. By this Blockchain provide security by giving the patients to access their own data rather than provider.

Open access
Big Data and Business Intelligence
Artificial Intelligence in Healthcare
Customer churn and segmentation
Original source
Apr 10, 2019¡International Journal of Research in Advent Technology
5 cites
Utlization of Blockchain in Medical Healthcare Record using Hyperledger Fabric

Vijayakumar, V., K.M. Sabarivelan, J. Tamizhselvan, B Ranjith ¡ 5 authors

Blockchain is a decentralized network technology. Blockchain consists of a number of blocks. The blocks are connected with other through a chain. Hence the name Blockchain. A block consists of a number of transactions. The links between the blocks are made up of hash values. The hash values are calculated using the transactions in a block and the hash value of the previous block. Healthcare is one of the biggest industry. It also remains as a industry which lacks transparency. At crucial situations the patients medical reports are not readily available. Interoperability between medical organizations is not available due to trust issues. Blockchain is a technology which can provide trust and transparency to its participants. Combining blockchain with healthcare can bring a huge change in the healthcare domain. By including frameworks like Hyperledger, we can provide an industrial standard to healthcare industry along with transparency and trust. All the medical data can be stored in a distributed ledger, which can be used at critical periods for examining report details. Blockchain also provides high security to the data. The data in the blockchain will remain tamper proof.

Open access
Brain Tumor Detection and Classification
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare
Original source
Apr 1, 2019¡Journal of Physics Conference Series
7 cites
Blockchain-based Intelligent Hospital Security and Data Privacy Construction

Qiuzi Huang, Shuyu Chen, Hui Zhao, Junhao Wen

With the rapid development of medical information services, the construction of intelligent hospitals is opening up a new mode of medical treatment in the health care industry. Medical data is gradually becoming more and more important, while it also faces some challenges, among which the most urgent problem to be solved is data security and privacy protection. In the construction of intelligent hospitals, the safety issues among the basic information of patients, the protection of medical information and inter-institutional information sharing have become the focus at this stage. Blockchain technology, with highly security, reliable architecture and algorithm design have operated stably in the financial industry for more than seven years. The related innovative technologies such as distributed ledgers, smart contracts, symmetric encryptions and consensus mechanisms are used widely in many fields. This paper will take the demonstration construction of Chongqing Intelligent Hospital as an example, which tries to combine the blockchain, homomorphic encryption and zero-knowledge proof technology to carry out the research on the security construction and data privacy protection of intelligent hospitals.

Open access
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Original source
Mar 15, 2019¡International Journal of Computer Applications
12 cites
B-DEC: Digital Evidence Cabinet based on Blockchain for Evidence Management

Eko Yunianto, Yudi Prayudi, Bambang Sugiantoro

Digital evidence handling and preservation are one stage of digital forensic process. This part is very crucial because digital evidence is the basis of digital forensic process. The credibility of digital evidence must be maintaned for law and court process. Process of preservation of digital evidence known as chain of custody (CoC). CoC is a document that used to ensure that digital evidence remains and does not change. Both during the investigation process until the completion of the forensic process. Electronic evidence documentation is different from digital evidence documentatation. The different are character and the metadata. Some adjustments need to be accomodated on system for digital evidence. Digital evidence is easy to change as well as its CoC document. It needs to be protected. A new technology is needed that can ensure integrity of digital evidence and CoC Document like the blockchain. In this study, digital evidence management will be built on the CoC concept with blockchain technology. More precisely, this design combines the framework of the Digital Evidence Bag (DEC) with blockchain technology. This prototype is known as the Blockchain Digital Evidence Bag (B-DEC). B-DEC utilizes the data storage integrity to accommodate digital evidence management that refers to DEC. In this case, the prototype will build on a smart contract based on Ethereum. The development of the DEC framework also will be adjusted to accommodate DEC applications in the blockchain.

Open access
Artificial Intelligence in Healthcare and Education
Blockchain Technology Applications and Security
Original source
Jan 1, 2019¡Tuwhera (Auckland University of Technology)
11 cites
A Review of Issues in Healthcare Information Management Systems and Blockchain Solutions

Alan Litchfield, Arshad Khan

Healthcare is a data-driven domain where a large volumes of data are created, accessed, stored, and disseminated daily. In this paper, issues such as security, privacy, data transparency, interoperability, data accessibility, user interface issues in healthcare information management systems are presented. In addition, blockchain technology related studies in healthcare information systems are discussed with the aim to find what issues in healthcare system present research opportunities using blockchains.

Open access
Organizational and Employee Performance
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare
Original source
Jan 1, 2019¡Journal of Emergencies Trauma and Shock
4 cites
Roadmap for the development of academic and medical applications of blockchain technology: Joint statement from OPUS 12 global and litecoin cash foundation

StanislawPeter Stawicki, SagarC Galwankar, Sebastian Clarke, Iain Craig ¡ 11 authors

Technological progress is reshaping multiple domains of human activity, from financial transactions to medical care.[1] This paradigm shift represents a global movement that will transform our lives for generations to come.[2] The democratization of decision-making capacity, including consensus-based mechanisms for transaction verification, will enable global implementation of projects that were previously not feasible because of the requirement for centralized control.[34] Blockchain represents a decentralized ledger technology that operates by consensus and serves to democratize decision-making processes and to disintermediate traditionally understood intermediaries.[1] According to Deutsche Bank forecasts, by mid-2020's, approximately 10% of the worldwide gross domestic product could be regulated by blockchain-based solutions.[5] It is estimated that more than $400 billion will be invested in this technology in 2019 to advance its capabilities.[6] Within this broader context, it is important to understand that cryptocurrencies and financial transactions constitute only one small aspect of the blockchain concept, which also incorporates areas like verification, transparency, encryption, and maintenance of data integrity.[378] Blockchain technology appears to be following a fairly typical pattern of adoption, with multiple early entrants into the increasingly crowded and competitive cryptocurrency space and the fast-growing sphere of blockchain-based applications.[91011] It is the latter that will help truly define, and be responsible for the societal impact of, “the era of distributed ledgers” that is under way.[1112] The primary goal of the strategic global partnership between Litecoin Cash Foundation (LCCF, https://litecoinca.sh/) and OPUS 12 Foundation, Inc. (O12FI, http://www.opus12.org/), is to leverage our collective resources to establish early leadership in the development and implementation of practical, real-life, blockchain-based solutions in academic and clinical medicine.[11314] The practicality of the dual blockchain utilization, featuring both currency and application layers, becomes apparent with the realization that the need for ongoing data processing relies on constant verification and encryption activity throughout the entire network of blockchain nodes.[113] Thus, the approach selected by the LCCF-O12FI consortium creates significantly more synergy than a single-track approach based on subcomponent strategy. Within this context, the technology provides not only a “digital wallet” functionality for currency exchange, but also different blockchain-based use cases incorporating academic and medical information. In one example, cell phones are ubiquitous in low- and middle-income countries (LMIC) whereas electronic health records are not. Older, less costly cell phone technology would suffice as only SMS capability is needed to utilize blockchain or cryptocurrency, enabling broad access to the populations of LMICs. Blockchain can support information exchange across disparate data types, while providing digital payments on a global scale and across borders. The functional dimension of introducing the primary currency feature of Litecoin Cash (LCC) cryptocurrency has the potential to bring tremendous benefits to the areas of the world where banking services (and infrastructure) are severely underdeveloped, yet basic components for the successful adoption of cryptocurrencies clearly exist (e.g., limited internet access and mobile devices capable of supporting blockchain transactions). Much like entire regions of the world that essentially “bypassed” landline-based telephony following the introduction of cellular networks, many localities stand to “bypass and leapfrog” traditional banking, and progress directly to distributed ledger technologies.[151617181920] There is growing recognition of the role of microeconomies and the critical need for efficient, dependable, accessible, safe, and scalable financial transactions and infrastructures, especially in low-resource regions, a topic that was recently recognized with a Nobel Prize in Economics.[2122] Of note, this does not necessarily preclude traditional banking firms from participation; however, they will need to adapt to new competitive pressures across economic realities for which high-resource environment models are not optimized. Ability to appropriately scale current blockchain capabilities will be critical to such implementations.[32324] Blockchain-based mechanisms also allow for crossover of monetary value from various loyalty cards and rewards programs, similar to currency exchange between different nations. Such reward points (mileage, car rental, and hotel stay) can then become an alternative subsidy for healthcare services. This can, for example, help establish a modernized barter system where a patient could use their “frequent flyer miles” to pay for medical costs, exchanging their reward points for “health care coins” through an intermediary exchange market. Institutions, such as nongovernmental organizations, could turn “flyer miles” used to shuttle staff between locations into vaccine and medical equipment purchases. Further, direct and real-time transparent payment for services in healthcare could lead to a reduction of both “intermediary” insurance companies and inefficiencies in the system. This streamlining would result in substantial healthcare savings, translating to lower costs, more access for patients, and decreased overhead with increased revenue for clinics, hospitals, and providers. Security of the blockchain (including various “side chains” and “layers”) is of paramount importance to ensuring trust and wider mainstream adoption of this technology.[252627] The inherent risk in the concept of distributed ledger “democratization” is the possibility of emerging inequality due to maldistribution of infrastructure responsible for the maintenance and ongoing operations of the blockchain.[2829] Within this broader topic area, our group previously described the risk of ill-intended, third-party actors to project massive bursts of “hashing power” and effectively take over the blockchain for a limited duration of time.[1] This, in turn, allows such destructive actors to “double spend” cryptocurrency output to the detriment of the broader populace.[114] To effectively prevent the risk of the blockchain being “hijacked,” the LCCF Developer Team devised an innovative paradigm of agent-based mining (e.g., the creation of new cryptocurrency) that helps ensure democratization of the LCC generation/transaction process while providing sustainable, long-term security of the distributed ledger.[14] Another significant advantage of this prototype mining technique is that it is not based on technologies that are becoming increasingly energy and resource inefficient, thus not requiring ever greater amounts of energy to generate diminishing amounts of block rewards (e.g., “coins”). The synergy between secure mining processes and the need for the highest possible levels of distributed ledger security creates a unique environment for the development of blockchain-based educational and medical applications. Our joint implementation framework of blockchain-based application layer includes clearly stated and reasonably achievable milestones, each defined within the broader contexts of adoption readiness and resource availability. Parallel to these developments will be the phased introduction of LCC as a voluntary medium of exchange for various international medical programs (IMPs) collaborating within our global network of institutions, providers, and clinical sites.[30] The initial step in the strategic LCCF-O12FI collaboration will be the development of a cryptography-based “Secure ID” (SID) that will serve as the foundation for the future developments. This SID will contain each user's unique identifying information, accessible only to the end-user (incorporating various best practices in cyber security such as 2-factor or multisource verification), and shareable for viewing and information verification only with end-user's designees. The SID will also serve as a “Secure Key” to access other, downstream blockchain-based applications including “Academic Activity Logger” (AAL) and “Credentialing Document Repository” (CDR). We will now discuss the development and implementation of AAL and CDR. The AAL will be the first step toward the integration of blockchain technology into real-life academic international medicine (AIM) applications. Powered by the global LCC network, the AAL will help record and track activities by faculty members, facilitating the categorization and quantification of academic efforts into the following subtypes: (a) teaching, (b) clinical medicine, (c) community/government interactions, (d) research, and (e) other/miscellaneous. Each entry will include the activity date/time/duration as well as basic description, with a number of generic entries available through a drop-down menu. Activities entered by academic faculty will then be analyzed periodically and will serve as a basis for resource mobilization and allocation. Access to the AAL will only be possible using the SID, thus making the AAL a logical extension and a springboard for subsequent LCC blockchain-based implementations. On this foundation, the CDR and ultimately a “basic electronic medical record” (BEMR, see below) will be constructed. Although the task of constructing a high-fidelity, immutable, and accurate ledger of academic activities will not be easy, certain steps can be taken to minimize the likelihood of “false claims.” Much like the blockchain-based cryptocurrency paradigm, a secure mechanism for consensus building and data verification can be constructed. Such a “network of trust” (NOT) is technically workable and analogous to how “pretty good privacy” keys were distributed at signing parties attended by people known to each other, and also similar to the way “secure socket layer” certificate authorities work.[3132] For example, if Party A's certificate is signed by some Party B who is trusted by Party C, Party C can trust Party A's certificate, etc. The next developmental step in our strategic plan will be the implementation of the CDR, where provider credentials will be securely uploaded and stored in decentralized fashion. These documents will follow predefined credentialing requirements by most institutions globally such as record of college education/graduation, professional school record/diploma, professional licensing/verification/certification, and any additional elements deemed important to the safe conduct of AIM efforts globally. Uploading of credentialing information will be voluntary, and access to this information will only be possible with the permission of the record owner, utilizing his or her unique SID. Optimally, this important credentialing instrument will help providers verify their identity, education, and qualifications, and ensure that appropriate standards are followed by all stakeholders. The end-result will be the provision of safe and efficient care to the patients worldwide. Similar to the academic activity verification process, there will be important challenges to consider before successfully implementing the global CDR. In principle, there will need to exist some form of “data onboarding” authority. This should be performed by a “verification agent” (e.g., independent organization/group) with equivalent authority to that which it takes to set up a legitimate record of specific type (e.g., a medical school diploma and medical board specialty certification). In terms of identity verification, for example, the level of diligence required is similar to that already present in “know your customer” legislation. The case for verifying professional credentials would be similar, including the process of independent data validation and certification. Again, this is technically possible with a NOT arrangement as mentioned above; however, there will be obvious limitations inherent to the workability of credential verification similar to traditional efforts already in place. If a practitioner is claiming to possess credentials from some credentialing provider, only that particular provider really has (and only should have) the authority to confirm that. At the same time, there must also be a mechanism to revoke trust and to hardwire time-defined recertification processes based on the expiry of records currently on file. The final step in the strategic LCCF-O12FI collaboration will be the development of a super secure, BEMR that could be deployed in low-resource environments, utilizing rudimentary portable device technology, and containing fundamental health information for each end-user. Much like the SID, information stored on the BEMR would be owned by the end-user and could be shared with healthcare providers only with the end-user's consent, requiring SID as the “Secure Key” to unlock information. We recognize the substantial challenges in the global implementation of this concept, especially with regards to the enormity of healthcare-related data (often from multiple systems and sources), the need for privacy, timely and accurate access, and verification of data. Other potential shortcomings, at this time, include the need for further development of the technology, limited availability of expert knowledge, significant gaps in public awareness, along with growth-related issues of scalability, security, and user adoption.[6] Nonetheless, it is our hope that the lessons learned from other blockchain application layers will serve as the foundation for successful development, evolution, and adoption of BEMR.[1] There are many other considerations related to blockchain technology implementations in healthcare. Although full discussion of such a broad topic is beyond the scope of this manuscript, certain key ramifications must be discussed in the context of the proposed O12FI-LCCF initiatives. Blockchain technology may be an important tool for increasing transparency of how charities collect and allocate funds, propelling a much leaner system that will benefit intended recipients to a much greater degree.[1] This application of the blockchain technology will help verify the integrity of an organization's operations such as the transparency regarding the proportion of contributions distributed to medical and educational causes versus the overhead. In turn, the public, philanthropic donors, and potential collaborators will be able to make more informed choices regarding where their contributions can be allocated most efficiently. Pharmaceutical companies may utilize blockchain to keep track of medications manufacturing and shipment, supply, expiration, and possible points of contamination.[33] By extension, similar technological approaches could be useful for tracking opiates in this age of epidemic prescription drug abuse.[34] In such cases, blockchain would make it easier to investigate and determine the source of access as the supply chain would become much more transparent. Various built-in data verification and safety features could also be used to prevent duplication of medications from different providers and other hazards that occur with polypharmacy. Furthermore, the same tracking approaches could be used to secure food supply chains and safeguard against disease outbreaks.[35] Contaminated food products could be quickly and more efficiently traced to specific farms, processing or packaging plants for immediate identification, and removal from circulation. When combined with potential applications for AIM and global health equity, blockchain-based applications could help catalyze further innovation. First, they can enable universal access to financial resources by removing third-party intermediaries and offering transparent, secure, and accountable means for AIM financing.[36] Next, they could help facilitate multilateral financing mechanisms dedicated to health system development and strengthening.[136] In addition, they could reduce fraud and corruption through the use of immutable, tamper-proof transaction ledgers.[136] Finally, entire new capital markets for healthcare data could be created, providing better access (and opportunities) to patients, institutions, governments, researchers, and other key stakeholders.[136] Additional benefit offered by any token with fixed or “capped” supply as a medium of international exchange – subject to harmonization with region-specific laws and regulations – is the noninflationary character of such cryptocurrency. This, in turn, may help provide end-users with a protective mechanism against inflation and loss of monetary value – a phenomenon experienced across many LMICs.[3738] In conclusion, the global partnership between OPUS 12 Foundation (including its allied partners and subsidiaries) and LCCF provides a unique platform for the parallel development of both global currency support framework and medical/educational application layer for the academic international medical community.

Open access
Artificial Intelligence in Healthcare and Education
Global Health and Surgery
Ethics in Clinical Research
Original source
Dec 3, 2018¡Journal of the American Medical Informatics Association
69 cites
Fair compute loads enabled by blockchain: sharing models by alternating client and server roles

Tsung-Ting Kuo, Rodney A. Gabriel, Lucila Ohno‐Machado

OBJECTIVE: Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risks such as single point of control. State-of-the-art blockchain-based methods remove the "server" role but can be less accurate than models that rely on a server. Therefore, we aim at developing a general model sharing framework to preserve predictive correctness, mitigate the risks of a centralized architecture, and compute the models in a fair way. MATERIALS AND METHODS: We propose a framework that includes both server and "client" roles to preserve correctness. We adopt a blockchain network to obtain the benefits of decentralization, by alternating the roles for each site to ensure computational fairness. Also, we developed GloreChain (Grid Binary LOgistic REgression on Permissioned BlockChain) as a concrete example, and compared it to a centralized algorithm on 3 healthcare or genomic datasets to evaluate predictive correctness, number of learning iterations and execution time. RESULTS: GloreChain performs exactly the same as the centralized method in terms of correctness and number of iterations. It inherits the advantages of blockchain, at the cost of increased time to reach a consensus model. DISCUSSION: Our framework is general or flexible and can also address intrinsic challenges of blockchain networks. Further investigations will focus on higher-dimensional datasets, additional use cases, privacy-preserving quality concerns, and ethical, legal, and social implications. CONCLUSIONS: Our framework provides a promising potential for institutions to learn a predictive model based on healthcare or genomic data in a privacy-preserving and decentralized way.

Open access
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
Machine Learning in Healthcare
Original source
Sep 19, 2018¡Journal of Higher Education Policy and Management
148 cites
Does competency-based education with blockchain signal a new mission for universities?

Peter Williams

New technologies and the knowledge economy are destabilising graduate professions, with artificial intelligence and the analysis of ‘big data’ making significant impacts on formerly secure jobs. Blockchain technology, offering automated secure credentialling of undergraduate students’ activities and achievements, may significantly erode existing systems of assessment. The challenge for universities will be not only to maintain the relevance of their curricula but also to manage erosion of their current near-monopoly in awarding degrees. This paper envisions a landscape in which universities must outsource parts of their course delivery and assessment in order to remain competitive. It examines a potentially sustainable mission strategy: to move away from narrow academic disciplines towards an authentic learning curriculum focusing on the development of students as whole persons with rounded educations. This paper examines implications for the academy of the convergence of artificial intelligence, data analytics and blockchain technology.

Open access
Online Learning and Analytics
Higher Education Learning Practices
Artificial Intelligence in Healthcare and Education
Original source