Recently, unmanned aerial vehicles (UAVs) are deployed in Novel Coronavirus Disease-2019 (COVID-19) vaccine distribution process. To address issues of fake vaccine distribution, real-time massive UAV monitoring and control at nodal centers (NCs), the authors propose SanJeeVni, a blockchain (BC)-assisted UAV vaccine distribution at the backdrop of sixth-generation (6G) enhanced ultra-reliable low latency communication (6G-eRLLC) communication. The scheme considers user registration, vaccine request, and distribution through a public Solana BC setup, which assures a scalable transaction rate. Based on vaccine requests at production setups, UAV swarms are triggered with vaccine delivery to NCs. An intelligent edge offloading scheme is proposed to support UAV coordinates and routing path setups. The scheme is compared against fifth-generation (5G) uRLLC communication. In the simulation, we achieve and 86% improvement in service latency, 12.2% energy reduction of UAV with 76.25% more UAV coverage in 6G-eRLLC, and a significant improvement of [Formula: see text]% in storage cost against the Ethereum network, which indicates the scheme efficacy in practical setups.
With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.
Open access
COVID-19 diagnosis using AI
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
Blockchain is an emerging technology based on a distributed digital ledger system. Decentralized trust is one of the key factors behind the blockchain-based system. The transparency of such a system is better than a conventional centralized ledger system. By using a blockchain-based transaction system, any business organization can harness key benefits like data integrity, confidentiality, and anonymity without involving any third party in control of the transactions. Since the blockchain is used in numerous applications, the horizon is expanding at an unprecedented pace. It was found that tracking COVID vaccination in a transparent and accountable way is an emerging need, especially after the pandemic outbreak around the world. The blockchain platform is a good match for such applications. In this study, a blockchain-based COVID-19 testing and vaccination tracking system, called COVAC, has been designed to manage the COVID testing and vaccination process for local organizations. The “Prototype Software Development" approach was used to determine the system requirements according to the practical knowledge obtained through the vaccine monitoring and screening tests process and then communicated with local healthcare facilities to determine whether these requirements were satisfied. The blockchain-based implementation ensured the system transparency, integrity, and security of data on COVID-19 testing and vaccination
Healthcare is a fundamental feature of smart cities. Artificial intelligence-based systems usage during COVID-19 pandemic like virtual doctors, online consultations, telemedicine has enhanced the capability of reaching the treatment to patients on time, with high efficiency in smart cities. Governments have implemented patient monitoring and disease control systems during the COVID pandemic to reduce the spread of infection. The dynamics of healthcare provisions are changed exceptionally with the advent of deep learning and blockchain methods. Advanced levels of image diagnosis, centralized EHRs, emergency care, and intelligence-based recommendation systems are enhanced using deep learning and blockchain technologies. Integration of IoT devices with smartphones has enabled us to receive alerts to monitor the elderly or needy people with chronic conditions. The adaptation of AI in clinical diagnosis and predictive care systems is remarkable. High precision robotic systems based on AI technology are used in delicate and complicated surgeries in many city hospitals across the globe.
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Ana Côrte-Real, Tiago Nunes, Clara Cruz Santos, Paulo Rupino da Cunha
The increase of forcibly displaced people worldwide is a challenge for health systems and their ability to provide access and equity in Health as a universal right. Health information systems should be strengthened to collect and disseminate migrant health data enabling analytics for strategic decisions.This Viewpoint focuses on blockchain technology as an emergent digital tool to improve communication and overcome gaps in medical data sharing, conceptualizing a global health space. Anchored in the security, privacy, and medico-legal regulation of medical data, Blockchain technology would empower inter-organizational services or workflows, in real-time, by the users, inside and outside the national health systems, anywhere in the world. As an innovative approach, this Viewpoint highlights the future directions in IT-supported health.
Open access
Blockchain Technology Applications and Security
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Ioannis Skalidis, Olivier Müller, Stéphane Fournier
The recent pandemic launched an acceleration in adopting telemedicine by cardiovascular health and triggered the flourishing of technological advancements, such as the metaverse, which is a novel interactive mix of digital worlds that leverages augmented reality with virtual reality. The CardioVerse represents a theoretical term for the embracement of the metaverse by cardiovascular medicine, encompassing the endless possibilities as well as the challenges that it holds and introduces new dimensions to disease education, prevention and diagnosis. Its applications are numerous, notably in enhancing medical visits, assisting cardiovascular interventions and reshaping the way medical education is provided. Although obstacles are expected in diverse domains such as security, technical, legislative and regulatory, the utilization of non-fungible tokens as a security asset for patient data appears as potential solution.
Open access
Artificial Intelligence in Healthcare and Education
Artificial intelligence (AI)-based studies have been carried out recently for the early detection of COVID-19. The goal is to prevent the spread of the disease and the number of fatal cases. In AI-based COVID-19 diagnostic studies, the integrity of the data is critical to obtain reliable results. In this paper, we propose a Blockchain-based framework called AIBLOCK, to offer the data integrity required for applications such as Industry 4.0, healthcare, and online banking. In addition, the proposed framework is integrated with Google Cloud Platform (GCP)-Cloud Functions, a serverless computing platform that automatically manages resources by offering dynamic scalability. The performance of five different machine learning models is evaluated and compared in terms of Accuracy, Precision, Recall, F-Score and Area under the curve (AUC). The experimental results show that decision trees gives the best results in terms of accuracy (98.4 %). Further, it has been identified that utilization of Blockchain technology can increase the load on memory.
Purpose The purpose of this study is to investigate the applications of blockchain in vaccine passport solution. The world is facing an unprecedented situation because of the COVID-19 pandemic. Many countries have witnessed sporadic lockdown and travel restrictions and it has marred trade and tourism. As the mass vaccination has started the life is slowly and steadily returning to true normal. Various countries are issuing vaccination passports to manage the immunization information and validate it. To realize vaccine-passport’s true potential, security and privacy concerns should be being taken care of. There is a need for studies to evaluate the emerging technology for the vaccine passport. Design/methodology/approach This study uses a mix of qualitative and quantitative methods to achieve its objective. This study uses a systematic literature review to analyze the potential of blockchain for vaccine passports. The case study of three different types of organizations implementing blockchain for vaccine passports was analyzed and results were presented. Last but not least, focus group discussion and search of secondary literature was to done to identify scientific, ethical and legal challenges associated with the use of vaccine passports. The method used for calculating the importance score of these challenges was analytical hierarchy process. Findings This study concludes that blockchain-based solutions are very suitable for vaccine passports and addresses the concern related to interoperability, privacy and security. The case study approach was used to elaborate the use of blockchain in three different options available for the vaccine. Last but not least, this study identifies the challenges faced by vaccine passport programs and suggests measures to overcome them. This study concludes that the ethical challenges associated with vaccine passports are more important and should be preferentially treated. Research limitations/implications This study is timely and will be he lpful for policymakers in designing the vaccine passport programs. It gives valuable insight to decision-makers evaluating technologies for the development of vaccine passport programs. This study identifies nine challenges to be tackled to making a vaccine passport program successful. Originality/value To the best of the author’s knowledge, this study is not able to find out a review on the use of blockchain technology for vaccine passports, and this study attempts to fill this gap. This study further discusses the cases of organizations that have implemented blockchain technology for vaccine passport programs.
While the onset of the COVID-19 pandemic has increased the popularity of home-based consultations, worries over privacy, high consultations costs, slow response times, and the burden on doctors due to the overwhelming number of COVID-19 cases have made current in-person and online models ineffective. In this study, we present an advanced, privacy-protected, artificial intelligence and blockchain-based consultation framework for minor medical conditions. Patients can post their medical queries anonymously on the blockchain network, which may be answered by any available medical professionals. The queries are sorted into their respective domains using naive Bayes and logistic regression. The consultations provided by medical specialists are evaluated based on their reputation, expertise, detail orientation, and the use of supporting documents, and rewards are given in accordance with the evaluation scheme. This fair and incentivized system provides cheaper and more accessible healthcare to patients, which is the need of the hour.
COVID-19 tracking tools or contact-tracing apps are developed by different countries to protect people from the pandemic situation. There are various technologies that can be used in this digital world to handle the patients in the lockdown period. Blockchain technology is one of the important techniques used in the medical field to maintain the patient’s medical records globally and for drug analysis etc. Blockchain is a public distributed ledger or document that is decentralized across many nodes. The public ledger records all the transactions, where the users in the blockchain network gets a copy of those transactions. This technology is used to track all the transactions in a secured and transparent manner. The patient records are also updated from diagnosis to current status. Here the records are shared but the privacy of a patient is maintained because the patient details are distributed as a hash value. These distributed records are immutable so nobody can change or corrupt the patient details in the blockchain because they can be easily identified. During an emergency, doctors can easily gather the health issues of a patient such as diabetes, blood pressure etc., from this public ledger, without any enquiry. Moreover, the doctor can track the previous medication details. This technology is also used in supply chain management of COVID drugs where the consumer can track from the production to dispatch, for identifying the fake medicine as well as anyone can gather the drug details. If any middlemen change the expiry date and price details it can be easily identified with the help of smart contracts. With this technology, Artificial Intelligence also plays a vital role in serving remote patients. Machine learning models are built by analyzing a massive amount of data. By using the distributed ledger, the data scientist can build the updated models continually using machine learning algorithms. The fusion of blockchain technology and machine learning models improves the accuracy of predictions in the health-care industry. This chapter describes the usage of blockchain technology with machine learning algorithms for serving the people.
Kalapatapu V. S. K. R. Shiva Kumar, Shriram K. Vasudevan, Nitin Vamsi Dantu
The COVID-19 pandemic has shocked the globe with an enormous number of people infected and a large death toll across several nations. A deadly virus has many victims but no country could stand out when it comes to producing a vaccine. The virus is so dangerous that it spreads rapidly through human contact and a person who is infected will infect around 600 people a month. It is so fast that more than 50,000 people are affected in one day in some countries and more than 1,000 people die in one day. There are many patients but not enough doctors and hospitals to treat them as the infection grows exponentially. No doctor can examine chest X-ray in thousands and have fast turnaround. We want to create a solution to reduce the workload on doctors, to easily determine whether a chest X-ray pneumonia is due to coronavirus or not, so that the rapid spread can be controlled and proper cure could be given to patients. Here we also add the distributed ledger technology called blockchain, which helps in monitoring the patient health data and thus it helps in having the complete history of the patient.
Subhi Alrubei, Edward A. Ball, Jonathan Rigelsford
In this study, a new blockchain protocol and a novel architecture that integrate the advantages offered by edge computing, artificial intelligence (AI), IoT end-devices, and blockchain were designed, developed, and validated. This new architecture has the ability to monitor the environment, collect data, analyze it, process it using an AI-expert engine, provide predictions and actionable outcomes, and finally share it on a public blockchain platform. For the use-case implementation, the pandemic caused by the wide and rapid spread of the novel coronavirus COVID-19 was used to test and evaluate the proposed system. Recently, various authors traced the spread of viruses in sewage water and studied how it can be used as a tracking system. Early warning notifications can allow governments and organizations to take appropriate actions at the earliest stages possible. The system was validated experimentally using 14 Raspberry Pis, and the results and analyses proved that the system is able to utilize low-cost and low-power flexible IoT hardware at the processing layer to detect COVID-19 and predict its spread using the AI engine, with an accuracy of 95%, and share the outcome over the blockchain platform. This is accomplished when the platform is secured by the honesty-based distributed proof of authority (HDPoA) and without any substantial impact on the devices’ power sources, as there was only a power consumption increase of 7% when the Raspberry Pi was used for blockchain mining and 14% when used to produce an AI prediction.
The COVID-19 pandemic has negatively affected aspects of human life and various sectors, especially the health sector. These conditions led to the creation of new patterns of life that people have had to deal with to reduce the spread of the epidemic by committing to social distancing, among others. Therefore, governments and technological organizations had to take advantage of technological developments in the current era to overcome these challenges that were created by these conditions. In this paper, we will discuss the role of the blockchain in combating the COVID-19 crisis. Then we will review the recently recorded blockchain-based research proposals to control the COVID-19 pandemic. Finally, we will highlight the challenges of using blockchain to combat the COVID-19 pandemic and find solutions to mitigate these challenges.
Prachurjya Kashyap, Syed Tafreed Numan, Amit Kumar, Rohit Paul · 7 authors
The use of blockchain technology as an end-to-end quality assurance monitoring tool is bound to become increasingly popular in the years to come, as evidenced by its successful adoption by such major entities as Walmart, Maersk, and DeBeers Jewelers. Blockchain addresses a significant constraint in supply chain management; namely the fact that often organizational information systems in a supply chain may not communicate fully, or at all. This constraint has traditionally limited the monitoring capacities of entities along a supply chain in some significant ways. This chapter discusses the various types and components of supply chains and identifies their major fraud risks, such as counterfeit goods, fraudulent billing, false claims, and misappropriation of assets. The chapter also provides an overview of some common food fraud schemes, such as product substitution, mislabeling, and adulteration. Most importantly, blockchain implementation benefits within various supply chains are outlined, discussed, and further demonstrated by looking at several case studies. The benefits of using blockchain technology for supply chain monitoring purposes include a higher level of quality assurance through more granular tracking and identity management, more effective and secure communication among various supply chain stakeholders, lower supply-chain related expenses – especially lower external failure costs – and more efficient administration of the entire supply chain.
Babak Mahdavi-Damghani, Robert Fraser, James Howell, Jon Sveinbjorn Halldorsson
A prospective study was undertaken to identify clinical, radiographical, haematological and biochemical profiles of severe acute respiratory syndrome (SARS) patients. A prediction rule, which demarcates low from high risk patients for SARS in an outbreak situation was developed. A total of 295 patients with unexplained respiratory illnesses, admitted to Queen Mary Hospital, Hong Kong SAR, China, in March to July 2003, were evaluated for clinical, radiological, haematological and alanine transaminase (ALT) data daily for 3 days after hospitalisation. In total, 44 cases were subsequently confirmed to have SARS by RT-PCR (68.2%) and serology (100%). The scoring system of attributing 11, 10, 3, 3 and 3 points to the presence of independent risk factors, namely: epidemiological link, radiographical deterioration, myalgia, lymphopenia and elevated ALT respectively, generated high and low-risk (total score 11–30 and 0–10, respectively) groups for SARS. The sensitivity and specificity of this prediction rule in positively identifying a SARS patient were 97.7 and 81.3%, respectively. The positive and negative predictive values were 47.8 and 99.5%, respectively. The prediction rule appears to be helpful in assessing suspected patients with severe acute respiratory syndrome at the bedside, and should be further validated in other severe acute respiratory syndrome cohorts.
During times of pandemics, the healthcare system may collapse due to the high demand for healthcare resources. Hence, there is a need for an online-automated platform that enables remote collection of symptoms from suspected patients, accurate and fast diagnostics, and data sharing among different entities within the healthcare system. However, many privacy and scalability challenges face such a platform. To address such challenges, we propose a custom-designed blockchain enabled platform that guarantees privacy-preservation via a mixture of group signature and random numbers that support anonymity of suspected patients and unlinkability of data while enabling mutual interaction between the suspected patient and the platform; provides automatic diagnostics via a deep neural network-based detector that runs on a smart contract within the blockchain; and offers access and administrative authority of the healthcare entities to the database of symptoms and their diagnoses via a consortium-based blockchain architecture. Experimental studies demonstrate a detection accuracy of 90 percent based on a deep convolutional recurrent neural network. A case study of 500 expected patients is examined giving promising results. Every patient can know the test results after only 14 min of submitting the data. The storage requirements are as low as 0.52 MB for each suspected patient and 0.6 MB for each hospital.
Mohamed Torky, Essam Goda, Václav Snåšel, Aboul Ella Hassanien
The fight against the COVID-19 pandemic still involves many struggles and challenges. The greatest challenge that most governments are currently facing is the lack of a precise, accurate, and automated mechanism for detecting and tracking new COVID-19 cases. In response to this challenge, this study proposes the first blockchain-based system, called the COVID-19 contact tracing system (CCTS), to verify, track, and detect new cases of COVID-19. The proposed system consists of four integrated components: an infection verifier subsystem, a mass surveillance subsystem, a P2P mobile application, and a blockchain platform for managing all transactions between the three subsystem models. To investigate the performance of the proposed system, CCTS has been simulated and tested against a created dataset consisting of 300 confirmed cases and 2539 contacts. Based on the metrics of the confusion matrix (i.e., recall, precision, accuracy, and F1 Score), the detection evaluation results proved that the proposed blockchain-based system achieved an average of accuracy of 75.79% and a false discovery rate (FDR) of 0.004 in recognizing persons in contact with COVID-19 patients within two different areas of infection covered by GPS. Moreover, the simulation results also demonstrated the success of the proposed system in performing self-estimation of infection probabilities and sending and receiving infection alerts in P2P communications in crowds of people by users. The infection probability results have been calculated using the binomial distribution function technique. This result can be considered unique compared with other similar systems in the literature. The new system could support governments, health authorities, and citizens in making critical decisions regarding infection detection, prediction, tracking, and avoiding the COVID-19 outbreak. Moreover, the functionality of the proposed CCTS can be adapted to work against any other similar pandemics in the future.
Wei Yan Ng, Tien-En Tan, Prasanth V H Movva, Andrew Hao Sen Fang · 12 authors
The COVID-19 pandemic has had a substantial and global impact on health care, and has greatly accelerated the adoption of digital technology. One of these emerging digital technologies, blockchain, has unique characteristics (eg, immutability, decentralisation, and transparency) that can be useful in multiple domains (eg, management of electronic medical records and access rights, and mobile health). We conducted a systematic review of COVID-19-related and non-COVID-19-related applications of blockchain in health care. We identified relevant reports published in MEDLINE, SpringerLink, Institute of Electrical and Electronics Engineers Xplore, ScienceDirect, arXiv, and Google Scholar up to July 29, 2021. Articles that included both clinical and technical designs, with or without prototype development, were included. A total of 85 375 articles were evaluated, with 415 full length reports (37 related to COVID-19 and 378 not related to COVID-19) eventually included in the final analysis. The main COVID-19-related applications reported were pandemic control and surveillance, immunity or vaccine passport monitoring, and contact tracing. The top three non-COVID-19-related applications were management of electronic medical records, internet of things (eg, remote monitoring or mobile health), and supply chain monitoring. Most reports detailed technical performance of the blockchain prototype platforms (277 [66·7%] of 415), whereas nine (2·2%) studies showed real-world clinical application and adoption. The remaining studies (129 [31·1%] of 415) were themselves of a technical design only. The most common platforms used were Ethereum and Hyperledger. Blockchain technology has numerous potential COVID-19-related and non-COVID-19-related applications in health care. However, much of the current research remains at the technical stage, with few providing actual clinical applications, highlighting the need to translate foundational blockchain technology into clinical use.
Abstract The latest epidemic of COVID‐19 has significantly impacted both human capital and the global economy, contributing to pandemics and severe global crises. Research into the creation and propagation of the disease is desperately needed. The Internet of Things, cloud computing, and artificial intelligence offer modern technology for real‐time processing for multiple applications such as healthcare applications, transport, traffic control, and so on blockchain is an evolving technology that will dramatically boost transaction protection in finance, supply chain, and other transaction networks. A stable and latency‐sensitive Quality of Service framework for COVID‐19 is the need of an hour. The purpose of this paper is to combine Fog computing and Artificial Intelligence with smart health to establish a reliable platform for early‐stage detection of COVID‐19 infection. A new ensemble‐based classifier is proposed to detect COVID‐19 patients. This research offers a blockchain platform to analyse how the unrelated cases of the COVID‐19 virus can be tracked and identified using peer‐to‐peer, time stamping, and the shared storage advantages of blockchain. In addition to growing patient loyalty, this would effectively enhance the consistency, flexibility, productivity, performance, and effectiveness of healthcare services. The idea of blockchain is used to establish security for the whole framework. Different implementations measure the efficiency of the suggested system. The performance of the proposed framework is evaluated in terms of delay, network usages, RAM usages, and energy consumption. On the other hand, the classifier is evaluated in terms of classifier accuracy, recall, precision, kappa static, and root mean square error. The result shows the performance of the proposed framework and classifier is always better than the traditional frameworks and classifiers.