Blockchain Papers

Wendy Charles

No abstract is available for this record.

Karamo Kanagi, Cheng‐Yuan Ku, Li-Kai Lin, Wen-Huai Hsieh

BACKGROUND: While electronic health records have been collected for many years in Taiwan, their interoperability across different health care providers has not been entirely achieved yet. The exchange of clinical data is still inefficient and time consuming. OBJECTIVES: This study proposes an efficient patient-centric framework based on the blockchain technology that makes clinical data accessible to patients and enable transparent, traceable, secure, and effective data sharing between physicians and other health care providers. METHODS: Health care experts were interviewed for the study, and medical data were collected in collaboration with Ministry of Health and Welfare (MOHW) Chang-Hua hospital. The proposed framework was designed based on the detailed analysis of this information. The framework includes smart contracts in an Ethereum-based permissioned blockchain to secure and facilitate clinical data exchange among different parties such as hospitals, clinics, patients, and other stakeholders. In addition, the framework employs the Logical Observation Identifiers Names and Codes (LOINC) standard to ensure the interoperability and reuse of clinical data. RESULTS: The prototype of the proposed framework was deployed in Chang-Hua hospital to demonstrate the sharing of health examination reports with many other clinics in suburban areas. The framework was found to reduce the average access time to patient health reports from the existing next-day service to a few seconds. CONCLUSION: The proposed framework can be adopted to achieve health record sharing among health care providers with higher efficiency and protected privacy compared to the system currently used in Taiwan based on the client-server architecture.

Jonathan Passerat‐Palmbach, Tyler Farnan, Mike McCoy, Justin D. Harris · 7 authors

Machine learning and blockchain technology have been explored for potential applications in medicine with only modest success to date. Focus has shifted to exploring the intersection of these technologies along with other privacy preserving encryption techniques for better utility. This combination applied to federated learning, which allows remote execution of function and analysis without the need to move highly regulated personal health information, seems to be the key to successful applications of these technologies to rapidly advance evidence-based medicine. We give a brief history of these technologies in medicine, outlining some of the challenges with successful use. We then explore a more detailed combination of usage with an emphasis on decentralizing or federating the learning process along with auditability and incentivization blockchain can allow in the machine learning process. Based on the cost-benefit analysis of previous efforts, we provide the framework for an advanced blockchain-orchestrated machine learning system for privacy preserving federated learning in medicine and a new utility in health. Six critical elements for this approach in the future will be:(a) Data and analytic processes discoverable on secure public blockchain while retaining privacy of the data and analytic processes(b) Value fabricated by generating data/compute matches that were previously illegal, unethical and infeasible(c) Compute guarantees provided by federated learning and advanced cryptography(d) Privacy guarantees provided by software (e.g., Homomorphic Encryption, Secure Multi-Party Computation, ...) and hardware (e.g., Intel SGX and AMD SEV-SNP) cryptography(e) Data quality incentivized via tokenized reputation-based rewards(f) Discarding of poor data accomplished via model poisoning attack prevention techniques.

Nogol Memari, Saranaz Abdollahi, Mahdi Maghrouni Ganzagh, Mehrdad Moghbel

Diabetes is a serious medical condition and regular screening for diabetes is of great importance as treatment options are most effective in the early stages of diabetes. Digital imaging of retina is considered as a low-cost method for screening and could be used in conjunction with computer-based image processing techniques to automatically detect early signs of diabetes utilizing diabetes-related pathologies visible in retinal fundus images. This research proposes a novel computer-assisted diagnosis (CAD) system for assisting with the screening of the population as up to 50% of the affected population are not aware of having diabetes. Moreover, these screenings are often carried out by an optometrist who receives some training with the patients being referred to an ophthalmologist if they show symptoms. Having a computer-assisted diagnosis system assisting the optometrist during the screening can greatly increase the detection rate for patients with diabetes by providing a second opinion and highlighting any suspicious pathologies. For achieving the highest detection rate possible, a hybrid machine learning approach is proposed in this research by combining Deep Learning with the AdaBoost classifier. The proposed computer-assisted diagnosis system starts with the segmentation of the blood vessels. Then, microaneurysms and exudates are segmentation from the image. Statistical and regional features are then extracted utilizing first, second, and higher-order image features. A Deep Learning framework will be utilized for extracting additional statistical image descriptors as a Deep Learning has superior contextual analysis capabilities compared to other machine learning techniques. Finally, the most informative features are selected by a minimal-redundancy maximal-relevance feature selection approach with an AdaBoost classifier analyzing all the features and informing the operator regarding the patient’s condition. Ethereum Swarm blockchain-based decentralized cloud file storage provides the proposed CAD users with a secure storage olution to access the patient information and related images. The sensitivity, specificity, and accuracy of the classification will be measured under clinical conditions. Healthcare, government, and public users would receive the most benefit from this project.

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>

Shiva Ahir, Dipali Telavane, Riya Thomas

The pandemic of Coronavirus Disease 2019 (COVID-19) is proliferating across the globe obnoxiously and it is the most heard buzzword in recent times. Every person ranging from older people, persons with disabilities, youth, indigenous people have become a part of this chain and are most likely to suffer in the upcoming chronology. Social distancing is likely to become a new norm where “Work from Home”, Online Lectures” and “Meetings” ensue on social media applications. Technology has always lent a helping hand for mankind's problems. The idea focuses on highlighting the advancements in technology in the midst of a bizarre situation. Deep Learning applications to detect the symptoms of COVID-19, AI based robots to maintain social distancing, Blockchain technology to maintain patient records, Mathematical modeling to predict and assess the situation and Big Data to trace the spread of the virus and other technologies. These technologies have immensely contributed to curtailing this pandemic. Strong will power, patience and optimistic guidelines catered by the respective government are some of the altercations to COVID-19.

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.

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.

Daniel Uzbelger Feldman, Connolly Terry, Mustafa A. Badi, C. Yesilsoy · 5 authors

No abstract is available for this record.

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.

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.

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

<sec> <title>BACKGROUND</title> 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. </sec> <sec> <title>OBJECTIVE</title> 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. </sec> <sec> <title>METHODS</title> 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. </sec> <sec> <title>RESULTS</title> 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. </sec> <sec> <title>CONCLUSIONS</title> 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. </sec> <sec> <title>CLINICALTRIAL</title> <p/> </sec>

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.

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.

Amin Fadaeddini, Babak Majidi, Mohammad Eshghi

No abstract is available for this record.

Vivian Brian Lobo, Jetso Analin, Ronald Melwin Laban, Shraddha S. More

Owing to enlarged digital data obtainability and artificial intelligence (AI) progressions, there are quite a few occasions that can be reconnoitered in healthcare. Deep learning (DL) and inductive transfer practices are turning healthcare data- such as phantasmagorias and videotapes-into powerful data sources for predictive analytics. At the present time, patients fail to have entree to his/her individual medicinal records and hang around ignorant of data importance or prominence. This paper directs to offer a gestalt of AI and blockchain and exhibit a roadmap for a blockchain-assisted decentralized bionetwork of private healthcare data to expediate new methodologies to drug discovery and precautionary healthcare. A protected and crystal-clear disseminated marketplace of personal data by means of blockchain and DL technology will circumvent challenges faced by authorities of a given healthcare system and restore custody all across private records that includes medicinal documents back to humans. It also proposes a novel type of utility cryptotoken named LifeCoin, which can be produced through the stationing of data on the blockchain-assisted open market to streamline transactions and expedite inventive reward schemes.

Sanskar Jain, Aditya Anand, Aman Gupta, Kushagra Awasthi · 6 authors

Today we have enormous amount of data available in every sector, with the advent of technology available, it is possible to provide solutions to many problems. In this paper we are going to provide solutions to the problems related to healthcare data management using Machine Learning and Blockchain. Extracting only the relevant information from the data is possible with the use of Machine Learning. This is done using trained algorithms. Once this data is stored, the next problem is Data sharing and its reliability. This is where Blockchain comes into picture. The consensus in Blockchain technology makes sure that data is legitimate and transactions are secure. Blockchain technology can potentially change health care management for the better by placing patient at the epicentre of the healthcare system and increasing the privacy and interoperability of health data. This paper focuses primarily on solving healthcare data management problems by using Blockchain technology and including some indispensable features using Machine Learning.

Morgan P. McBee, Chad Wilcox

No abstract is available for this record.

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.

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.

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.

Selwan Abdullah, Steven Rothenberg, Eliot L. Siegel, Woojin Kim

No abstract is available for this record.

Wei Liu, Qinyong Yu, Zesong Li, Zeyuan Li · 6 authors

The number of healthcare insurance frauds increases year by year, which causes tremendous concern in society. Healthcare insurance fraud exists in various forms, such as falsifying information, concealing third-party liability, falsified electronic bill and so on. The current healthcare insurance system requires a lot of manpower and resources which bears heavily on the system. In this paper, referring to the various forms of healthcare insurance fraud, we propose a healthcare insurance anti-fraud system based on blockchain. The system adopts the architecture based on cloud computing. And organizations such as 120-command centers, public security traffic control departments, judicial organs, healthcare insurance agencies, etc., are included in the blockchain system. And the data of medical expenses, prescriptions, inspection reports, treatment records in the medical process is employed to establish a healthcare insurance blockchain. Based on the blockchain, the system provides medical process inspection service, excessive medical behavior analysis, third-party liability inspection service and medical invoice data review service.

Jingqiu Gong, Shaofu Lin, Jingwen Li

Data provenance technology and right confirmation technology have received great attention in recent years, because data exposure and abuse have been a serious problem with the rapid development of smart device and hospital. Based on the traceability and unchangeable properties of blockchain, we propose a model of personal health-related application data provenance and use smart contract to ensure right confirmation. We find that the user condition and use-right of personal health data can be effectively confirmed by using data provenance. In addition, the possible future extensions to personal health data on provenance are discussed.