Blockchain Papers

European Society of Radiology (ESR), Elmar Kotter, Luis Martí‐Bonmatí, Adrian P. Brady · 5 authors

Blockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different "authors" including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

Faheem Ahmad Reegu, Salwani Mohd Daud, Zaid Hakami, Kaiser Kariem Reegu · 5 authors

The digital archive of the patient's personal health records is the Electronic Health Record (EHR) that has various advantages. For finding the best solution to resolve the associated issues, a detailed study is required to utilize modern technologies and standards so that these issues and errors can be minimized. There are several issues associated with implementing the EHR, including data management, privacy, and patient data security. This research aims to examine the use of the Blockchain in EHR frameworks as per the national and international standards of EHR to reduce the associated issues. A blockchain-based framework can be successful in solving the current challenges in EHR. It allows storing, sharing, managing, controlling, and maintaining patient information between healthcare providers. The study illustrated the related difficulties of integrating EHR and proposed Blockchain as a solution to help manage the records and preserve privacy, confidentiality, usability and protection of patient-related details. This study helps to understand the current challenges better and help in the proper implementation of Blockchain technology in EHR.

Jan Witowski, Jongmum Choi, Soomin Jeon, Doyun Kim · 9 authors

Current research on medical image processing relies heavily on the amount and quality of input data. Specifically, supervised machine learning methods require well-annotated datasets. A lack of annotation tools limits the potential to achieve high-volume processing and scaled systems with a proper reward mechanism. We developed MarkIt, a web-based tool, for collaborative annotation of medical imaging data with artificial intelligence and blockchain technologies. Our platform handles both Digital Imaging and Communications in Medicine (DICOM) and non-DICOM images, and allows users to annotate them for classification and object detection tasks in an efficient manner. MarkIt can accelerate the annotation process and keep track of user activities to calculate a fair reward. A proof-of-concept experiment was conducted with three fellowship-trained radiologists, each of whom annotated 1,000 chest X-ray studies for multi-label classification. We calculated the inter-rater agreement and estimated the value of the dataset to distribute the reward for annotators using a crypto currency. We hypothesize that MarkIt allows the typically arduous annotation task to become more efficient. In addition, MarkIt can serve as a platform to evaluate the value of data and trade the annotation results in a more scalable manner in the future. The platform is publicly available for testing on https://markit.mgh.harvard.edu.

Daniel Cabrera, Christopher P Nickson, Damian Roland, Elissa Hall · 5 authors

<sec> <title>UNSTRUCTURED</title> Current health professions education (HPE) institutions are based on an assembly-line hierarchical structure. The last decade has witnessed the advent of sophisticated networks allowing the exchange of information and educational assets. Blockchain provides an ideal data management framework that can support high-order applications such as learning systems and credentialing in an open and a distributed fashion. These system management characteristics enable the creation of a distributed autonomous organization of learning (DAOL). This new type of organization allows for the creation of decentralized adaptive competency curricula, simplification of credentialing and certification, leveling of information asymmetry among educational market stakeholders, assuring alignment with societal priorities, and supporting equity and transparency. </sec>

Dominique Vervoort, Camila R. Guetter, Alexander W. Peters

Health disparities remain vast around the world and are perpetuated by error-prone information technology systems, administrative inefficiencies and wasteful global health spending. Blockchain technology is a novel, distributed peer-to-peer ledger technology that uses unique, immutable and time-stamped blocks of records or sets of data that are linked as chains through cryptography to more reliably and transparently store and transfer data. Various industries have successfully leveraged blockchain technology to disintermediate and reduce costs, but its use in healthcare and global health has remained limited. In this narrative review, we describe blockchain technology and elaborate on the experiences and opportunities for leveraging blockchain within global health in terms of cryptocurrencies and health financing, supply chain management, health records, identification and verification, telehealth and misinformation. We conclude each section with an analysis of the restrictions imposed by the COVID-19 pandemic to highlight blockchain’s unique opportunities for improving healthcare services and access to care during future pandemics or natural disasters.

Tim Joda, Nikolaos Pandis

No abstract is available for this record.

Anjum Khurshid, Cole Holan, Cody Cowley, Jeremiah Alexander · 9 authors

OBJECTIVE: Healthcare systems suffer from a lack of interoperability that creates "data silos," causing patient linkage and data sharing problems. Blockchain technology's unique architecture provides individuals greater control over their information and may help address some of the problems related to health data. A multidisciplinary team designed and tested a blockchain application, MediLinker, as a patient-centric identity management system. METHODS: The study used simulated data of "avatars" representing different types of patients. Thirty study participants were enrolled to visit simulated clinics, and perform various activities using MediLinker. Evaluation was based on Bouras' criteria for patient-centric identity management and on the number of errors in entry and sharing of data by participants. RESULTS: Twenty-nine of the 30 participants completed all study activities. MediLinker fulfilled all of Bouras' criteria except for one which was not testable. A majority of data errors were due to user error, such as wrong formatting and misspellings. Generally, the number of errors decreased with time. Due to COVID-19, sprint 2 was completed using "virtual" clinic visits. The number of user errors were less in virtual visits than in personal visits. DISCUSSION: The evaluation of MediLinker provides some evidence of the potential of a patient-centric identity management system using blockchain technology. The results showed a working system where patients have greater control over their information and can also easily provide consent for use of their data. CONCLUSION: Blockchain applications for identity management hold great promise for use in healthcare but further research is needed before real-world adoption.

Ya Gong, Shi Qiuxia, Deling Duan, Zhiqiang Liu

In recent years, the IVD industry has developed rapidly based on the increasing market demand, and plays an important role in disease prevention, clinical diagnosis, health monitoring and guiding treatment. Therefore, followed quality and safety issues are highly concerned. The unique advantages of blockchain technology, decentralization, distrust and non-tampering, can write into trusted node data in every link covering production, circulation and usage of IVD reagents, and establish a distributed ledger with full backup, which makes the anti-conterfeiting and traceability for IVD reagents possible. We discuss whole process intelligent tracing system for IVD reagents based on blockchain technology. Through the strong mechanism of pre-supervision and post-punishment, the source of reagents can be traced, quality and responsibility can be investigated, and the medical inspection quality and diagnostic safety can be guarded.

Satyabrata Aich, Nday Kabulo Sinai, Saurabh Kumar, Mohammed Al Ali · 7 authors

For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage.

Lamine Amour, Matthieu Quiniou, Sara Tucci-Piergiovanni, Hichem Bourak · 5 authors

No abstract is available for this record.

S. Vijayalakshmi, SAVITA SAVITA, S. P. Gayathri, S. Janarthanan

No abstract is available for this record.

Wendy Charles

No abstract is available for this record.

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.

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

&lt;div&gt;&lt;b&gt;Objectives: &lt;/b&gt;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.&lt;/div&gt;&lt;div&gt;&lt;b&gt;Methods: &lt;/b&gt;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.&lt;/div&gt;&lt;div&gt;&lt;b&gt;Results: &lt;/b&gt;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.&lt;/div&gt;&lt;div&gt;&lt;b&gt;Conclusions: &lt;/b&gt;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.&lt;/div&gt;&lt;div&gt; &lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;

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.