Machine learning (ML) has penetrated various fields in the era of big data. The advantage of collaborative machine learning (CML) over most conventional ML lies in the joint effort of decentralized nodes or agents that results in better model performance and generalization. As the training of ML models requires a massive amount of good quality data, it is necessary to eliminate concerns about data privacy and ensure high-quality data. To solve this problem, we cast our eyes on the integration of CML and smart contracts. Based on blockchain, smart contracts enable automatic execution of data preserving and validation, as well as the continuity of CML model training. In our simulation experiments, we define incentive mechanisms on the smart contract, investigate the important factors such as the number of features in the dataset (num_words), the size of the training data, the cost for the data holders to submit data, etc., and conclude how these factors impact the performance metrics of the model: the accuracy of the trained model, the gap between the accuracies of the model before and after simulation, and the time to use up the balance of bad agent. For instance, the increase of the value of num_words leads to higher model accuracy and eliminates the negative influence of malicious agents in a shorter time from our observation of the experiment results. Statistical analyses show that with the help of smart contracts, the influence of invalid data is efficiently diminished and model robustness is maintained. We also discuss the gap in existing research and put forward possible future directions for further works.
Much has been written about the fourth industrial revolution’s (4IR) contributions to and its impact on higher education (HE). In addition, review studies have been conducted on the contributions of 4IR technologies to and on their impact on HE. Most of these studies have reviewed single 4IR technologies in isolation as attested to by the review studies cited in the current study. Against this backdrop, the current study reviewed, discussed, and synthesized the applications, prospects, and challenges of artificial intelligence (AI), robotics, and blockchain at given higher education institutions (HEIs) between 2013 and 2019 as reported by 26 selected journal articles. Employing a slightly modified version of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines for searching and screening, three of the findings of this study are worth mentioning. Firstly, the dominant AI technologies for learning are chatbots, and AI holds the prospect of personalized, scalable, and affordable learning. Secondly, the applications of robotics are exploratory in nature, and have a meta-teaching and a meta-learning orientation. Thirdly, some of the applications of blockchain relate to digital grading, digital credentialing and digital certification, and to real-time contracting and time stamping of learning. The implications of this review are that the three sets of technologies reviewed, have a lot applications for HE, barring the challenges that have been outlined.
Open access
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Due to the high transmission rate and high pathogenicity of the novel coronavirus (COVID-19), there is an urgent need for the diagnosis and treatment of outbreaks around the world. In order to diagnose quickly and accurately, an auxiliary diagnosis method is proposed for COVID-19 based on federated learning and blockchain, which can quickly and effectively enable collaborative model training among multiple medical institutions. It is beneficial to address data sharing difficulties and issues of privacy and security. This research mainly includes the following sectors: in order to address insufficient medical data and the data silos, this paper applies federated learning to COVID-19's medical diagnosis to achieve the transformation and refinement of big data values. With regard to third-party dependence, blockchain technology is introduced to protect sensitive information and safeguard the data rights of medical institutions. To ensure the model's validity and applicability, this paper simulates realistic situations based on a real COVID-19 dataset and analyses problems such as model iteration delays. Experimental results demonstrate that this method achieves a multiparty participation in training and a better data protection and would help medical personnel diagnose coronavirus disease more effectively.
Open access
Privacy-Preserving Technologies in Data
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Md Rahat Ibne Sattar, Md. Thowhid Bin Hossain Efty, Taiyaba Shadaka Rafa, Tusar Das · 8 authors
Nowadays, the online platform has been used by many educational institutions, to conduct tests, especially for secondary to tertiary level students. The most popular online test program is run by providing a user id and password to the candidates, and subsequently, they log in to the given web page to answer the questions. However, this system has a lot of bugs, the password can be misused followed by cheating in the test. This shows the importance of a secure system being implemented to avoid such a problem. This paper presents a blockchain framework that secures the online examination system. The proposed framework has been used to secure a data management system that connects to existing educational data. Institutions can simply compile their data history without requiring a copy from the central servers. The proposed blockchain framework improves data security and removes any potential cheating between users or third-party institutions that access applications and services. In this regard, this study provides a secured framework for conducting and evaluating subject tests to ensure consistency between student and server, and secure delivery of questionnaire from the server.
Open access
Academic integrity and plagiarism
Artificial Intelligence in Healthcare and Education
The term blockchain is mainly regarded as the distributed transaction which is mainly comprised of different blocks, and each set tends to represent the data that are being associated with the previous blocks. The blockchain is mainly managed through peer-to-peer networks which comparatively involves in adhering to the protocol of authenticating various blocks to form the blockchain. The usage of blockchain technology has been increasingly used in different fields, and healthcare services are now using blockchain for better patient delivery, detecting disease, and other aspects. The scope of the proposed study is that this study has exploited the function of a blockchain-enabled big data network to support medical professionals in giving better treatment modalities and delivering better patient care. The application of a new generation of smart block chains such as Ethereum and NEM is now offering better services and features in creating blockchain-based healthcare data management and hence support healthcare centers, medical practitioners, nurses, radiologists, and patients for better healthcare management. The application of blockchain technology in big data networks supports adding more value as it results in enhanced data quality, accessibility, and support in creating better security and safety of data and information, which is highly essential in the medical industry. Blockchain technology enables big data technologies enabled in supporting medical practitioners in addressing various healthcare ailments; one of the major diseases impacting many people around the world is diabetes. Patients with such ailments tend to generate more data and information related to the disease and health-related aspects. Hence, this information requires being maintained and analyzed, so that superior healthcare services can be provided. This study is more involved in the investigation of blockchain technology through a big data network enabled in offering better care for elderly individuals who have been affected due to diabetes, the researchers propose to choose a questionnaire method to collect the data from nearly 169 respondents, and these data were then analyzed using SPSS data package. The analyst used percentage analysis, correlation analysis, and chi-square test to analyze the data which has been collated by the researchers. The results and discussion show in detail the major aspects of blockchain technology in supporting healthcare professionals for better diabetes care management for elderly individuals.
Abstract: The interchange of electronic health data across healthcare facilities is made possible via the health information exchange program. There is a potential for data manipulation in this. This article primarily focuses on using "Blockchain," i.e. one of the greatest technologies, to secure medical health data. Blockchain has demonstrated its outstanding qualities in the field of cryptocurrencies like bitcoin and Ethereum. This study employs the Secure Hash Algorithm (SHA), Simple Mail Transfer Protocol (SMTP), and AES Rijndael Algorithm (SMTP). Additionally, using the Naïve Bayes method, we forecast many heart illnesses related to this work.
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
Abstract Healthcare institutions are progressively integrating artificial intelligence (AI) into their operations. The extraordinary potential of AI is restricted by insufficient medical data for AI model training and adversarial attacks wherein attackers perturb the dataset by adding some noise to it, which leads to the malfunctioning of the AI models, and a lack of trust caused by the opaque operational approach it employs. This Systematic Literature Review (SLR) is a state‐of‐the‐art survey of the research on blockchain technology for securing AI‐integrated healthcare applications. The most relevant articles from the Scopus and Web of Science (WoS) databases were identified using the PRISMA model. Most of the existing literature is about protecting the healthcare data used by AI‐based healthcare systems using blockchain technology, but the modality of data (text, images, audio, and sound) was not specifically mentioned. Information on protecting the training phase and model deployment for AI‐based healthcare systems considering the variations in feature extraction based on the modality of data was also not clearly specified. Hence, the three subfields of AI, namely, natural language processing (NLP), computer vision, and acoustic AI are further studied to identify security loopholes in its implementation pipeline. The three phases, namely the dataset, the training phase, and the trained models need to be protected from adversaries to avoid malfunctioning of the deployed AI models. The nature of the data processed by NLP, computer vision, and acoustic AI, underlying deep neural network (DNN) architectures, the complexity of attacks, and the perceivability of attacks by humans are analyzed to identify the need for security. A blockchain solution for AI‐based healthcare systems is synthesized based on the findings that have demonstrated the distinctive technological features of blockchains. It offers a solution for the privacy and security issues encountered by NLP, computer vision, and acoustic AI to boost the widespread adoption of AI applications in healthcare.
Sonali Vyas, Mohammad Shabaz, Prajjawal Pandit, L. Rama Parvathy · 5 authors
Over the last decade, the healthcare sector has accelerated its digitization and electronic health records (EHRs). As information technology progresses, the notion of intelligent health also gathers popularity. By combining technologies such as the internet of things (IoT) and artificial intelligence (AI), innovative healthcare modifies and enhances traditional medical systems in terms of efficiency, service, and personalization. On the other side, intelligent healthcare systems are incredibly vulnerable to data breaches and other malicious assaults. Recently, blockchain technology has emerged as a potentially transformative option for enhancing data management, access control, and integrity inside healthcare systems. Integrating these advanced approaches in agriculture is critical for managing food supply chains, drug supply chains, quality maintenance, and intelligent prediction. This study reviews the literature, formulates a research topic, and analyzes the applicability of blockchain to the agriculture/food industry and healthcare, with a particular emphasis on AI and IoT. This article summarizes research on the newest blockchain solutions paired with AI technologies for strengthening and inventing new technological standards for the healthcare ecosystems and food industry.
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
There is increasing recognition about health-oriented datasets that could be regarded as intangible assets: distinct assets with future economic benefits but without physical properties. While health-oriented datasets - particularly health records - are ascribed monetary value on the black market, there are few established methods for assessing the value for legitimate research and business purposes. The emergence of blockchain has created new commercial opportunities for transferring assets without intermediaries. Therefore, blockchain is proposed as a medium by which research datasets could be transacted to provide future value. For authorized individuals to verify their transactions, blockchain methodologies offer security, auditability, and transparency. The authors share data valuation methodologies consistent with accounting principles and include discussions of black market valuation of health data. Furthermore, this article describes blockchain-based methods of managing real-time payment/micropayment strategies.
Mustafa Abdul‐Moheeth, Muhammad Usman, Daniel Toshio Harrell, Anjum Khurshid
Background: The current healthcare ecosystem in the United States is plagued by inefficiencies in transitions of patient care between healthcare providers due in large part to a lack of interoperability among the many electronic medical record (EMR) systems that exist today. Both providers and patients experience significant frustration due to the negative effects of increased costs, unnecessary administrative burden, and duplication of services that occur because of data fragmentation in the system. Blockchain technology provides a potential solution to mitigate or eliminate these gaps by allowing for exchange of healthcare information that is distributed, auditable, immutable, and respectful of patient autonomy. Our multidisciplinary team identified key tasks required for a transition of care to design and develop a blockchain application, MediLinker, which served as a patient-centric identity management system to address issues of data fragmentation ultimately aiding in the delivery of high-value care services. Methods: The MediLinker application was evaluated for its ability to accomplish various key tasks needed for a successful transition of patient care in an outpatient setting. Our team created 20 unique patient use cases covering a diversity of medical needs and social circumstances that were played out by participants who were asked to perform various tasks as they received case across a simulated healthcare ecosystem composed of four clinics, a research institution, and other ancillary public services. Tasks included, but were not limited to, clinic enrollment, verification of identity, medication reconciliation, sharing insurance and billing information, and updating demographic information. With this iteration of MediLinker, we specifically focused on the functionality of digital guardianship and patient revocation of healthcare information. In addition, throughout the simulation, we surveyed participant perceptions regarding the use of MediLinker and blockchain technology to better ascertain comfortability and usability of the application. Results: Quantitative evaluation of simulation results revealed that MediLinker was able to successfully accomplish all seven clinical scenarios tested across the 20 patient use cases. MediLinker successfully achieved its goal of patient-centered interoperability as participants transitioned their simulated healthcare data, including COVID-19 vaccination status and current medications, across the four clinic sites and research institution. In addition to completing all key tasks designated, all eligible participants were able to enroll with and subsequently revoke data access with our simulated research site. MediLinker had a low data-entry error rate, with most errors occurring due to work-flow vulnerabilities. Our qualitative analysis of user perceptions indicated that comfortability and trust with blockchain technology, such as MediLinker, grew with increased education and exposure to such technology. Conclusions: The ubiquitous problem of data fragmentation in our current healthcare ecosystem has placed considerable strain on providers and patients alike. Blockchain applications for health identity management, such as MediLinker, provide a viable solution to stem the inefficiencies that exist today. The interoperability that MediLinker provided across our simulated healthcare system has the potential to improve transitions of care by sharing key aspects of healthcare information in a timely, secure, and patent-centric fashion allowing for the delivery of consistent and personalized high value care. Blockchain technologies appear to face similar challenges to widespread adoption as other novel interventions, namely recognition, trust, and usability. Further development and scaling are required for such technology to realize its full potential in the real world and transform the practice of modern health care.
Open access
Electronic Health Records Systems
Artificial Intelligence in Healthcare and Education
Jean Marie Vianney Nkurunziza, Jean Claude Udahemuka, Jean Baptiste Dusenge, Francine Umutesi
Healthy population is regarded as the most valuable asset of any country. Unfortunately, the health challenges that hinder mankind's wellbeing are enormously increasing. Examples include but are not limited to: the diversity of emerging diseases afflicting the global population, the projected demographic growth of elderly people who need consistent monitoring, the deficiency in medical staff, the lower density of physicians, and the challenging geographical location of the population from healthcare providers. The mitigation of such health challenges calls for novel technologies to improve patient outcomes. In this article, seven emerging technologies, namely: Wearable Devices and Internet of Things, Artificial Intelligence, Blockchain Technology or Distributed Ledger Technology, Robotics Technology, Telehealth and Telemedicine, Big Data Technology and Nanomedicine have been highlighted. For each discussed technology, its historical background, development drivers, market status and trends, significance to healthcare, key player companies, and associated challenges have been presented. The information contained in this paper was collected from different journal articles, websites, reports, conference proceedings, and books. It was observed that though the technologies discussed in this article show growth at different rates, healthcare technology development and implementation are very promising in revolutionizing the health sector and improving the health of the population. Therefore, healthcare providers and countries are recommended to put in place Healthcare Technology Assessment Programs to help them collect data regarding the technology efficacy, relevance, safety, outcomes, and alternative technologies towards better planning for healthcare services improvement.
This study highlights several challenges along with the potential and need for blockchain technology implementation in the Malaysian healthcare industry context. The systematic review unearths potential technological, organizational, environmental, individual-level acceptance concerns and challenges associated with deploying blockchain to support electronic health records. As such, this research serves as the starting point for a chain of studies aiming at detecting, analyzing, and responding to such demands. The study is expected to guide policy and decision-making procedure of a secure and resilient health information exchange for healthcare stakeholders of developing nations such as Malaysia.
Blockchain usage in healthcare, in radiology, in particular, is at its very early infancy. Only a few research applications have been tested, however, blockchain technology is widely known outside healthcare and widely adopted, especially in Finance, since 2009 at least. Learning by history, radiology is a potential ideal scenario to apply this technology. Blockchain could have the potential to increase radiological data value in both clinical and research settings for the patient digital record, radiological reports, privacy control, quantitative image analysis, cybersecurity, radiomics and artificial intelligence.Up-to-date experiences using blockchain in radiology are still limited, but radiologists should be aware of the emergence of this technology and follow its next developments. We present here the potentials of some applications of blockchain in radiology.
Open access
Advanced X-ray and CT Imaging
Radiomics and Machine Learning in Medical Imaging
Artificial Intelligence in Healthcare and Education
The misuse of health data stored in the Electronic Health Record (EHR) system can be uncontrolled. For example, mishandling of privacy and data security related to Corona Virus Disease-19 (COVID-19), containing patient diagnosis and vaccine certificate in Indonesia. We propose a system framework design by utilizing the InterPlanetary File System (IPFS) and Blockchain technology to overcome this problem. The IPFS environment supports a large data storage with a distributed network powered by Ethereum blockchain. The combination of this technology allows data stored in the EHR to be secure and available at any time. All data are secured with a blockchain cryptographic algorithm and can only be accessed using a user's private key. System testing evaluates the mechanism and process of storing and accessing data from 346 computers connected to the IPFS network and Blockchain by considering several parameters, such as gas unit, CPU load, network latency, and bandwidth used. The obtained results show that 135205 gas units are used in each transaction based on the tests. The average execution speed ranges from 12.98 to 14.08 GHz, 26 KB/s is used for incoming, and 4 KB/s is for outgoing bandwidth. Our contribution is in designing a blockchain-based decentralized EHR system by maximizing the use of private keys as an access right to maintain the integrity of COVID-19 diagnosis and certificate data. We also provide alternative storage using a distributed IPFS to maintain data availability at all times as a solution to the problem of traditional cloud storage, which often ignores data availability. Doi: 10.28991/esj-2021-SP1-013 Full Text: PDF
With the increasing number of coronavirus disease 2019 cases and worldwide vaccination coverage, 'vaccination passports' (vaccination certificates) may become a required permit for global travel, thereby supporting economic recovery. On March 7, 2021, Wang Yi, State Councilor and Foreign Minister of China, announced the launch of the Chinese version of an 'international travel health certificate' at a press conference of the National People's Congress and the Chinese Political Consultative Conference. He proposed a feasible 'Chinese solution' for promoting the recovery of the global economy and the facilitation of cross-border travel, and hoped that the international travel health certificate and vaccination passport can be mutually authenticated. The Israeli government issued a
Open access
Artificial Intelligence in Healthcare and Education
Electronic health records (EHRs) are digitally saved health records that provide information about a person’s health. EHRs are generally shared among healthcare stakeholders, and thus are susceptible to power failures, data misuse, a lack of privacy, security, and an audit trail, among other problems. Blockchain, on the other hand, is a groundbreaking technology that provides a distributed and decentralized environment in which nodes in a list of networks can connect to each other without the need for a central authority. It has the potential to overcome the limits of EHR management and create a more secure, decentralized, and safer environment for exchanging EHR data. Further, blockchain is a distributed ledger on which data can be stored and shared in a cryptographically secure, validated, and mutually agreed-upon manner across all mining nodes. The blockchain stores data with a high level of integrity and robustness, and it cannot be altered. When smart contracts are used to make decisions and conduct analytics with machine-learning algorithms, the results may be trusted and unquestioned. However, Blockchain is not always indestructible and suffers from scalability and complexity issues that might render it inefficient. Combining AI and blockchain technology can handled some of the drawbacks of these two technical ecosystems effectively. AI algorithms rely on data or information to learn, analyze, and reach conclusions. The performance of AI algorithms is enhanced through the data obtained from a data repository or a reliable, secure, trustworthy, and credible platform. Researchers have identified three categories of blockchain-based potential solutions for the management of electronic health records: conceptual, prototype, and implemented. The purpose of this research work is to conduct a Systematic Literature Review (SLR) to identify and assess research articles that were either conceptual or implemented to manage EHRs using blockchain technology. The study conducts a comprehensive evaluation of the literature on blockchain technology and enhanced health record management systems utilizing artificial intelligence technologies. The study examined 189 research papers collected from various publication categories. The in-depth analysis focuses on the privacy, security, accessibility, and scalability of publications. The SLR has illustrated that blockchain technology has the potential to deliver decentralization, security, and privacy that are frequently lacking in traditional EHRs. Additionally, the outcomes of the extensive analysis inform future researchers about the type of blockchain to use in their research. Additionally, methods used in healthcare are summarized per application area while their pros and cons are highlighted. Finally, the emphasized taxonomy combines blockchain and artificial intelligence, which enables us to analyze possible blockchain and artificial intelligence applications in health records management systems. The article ends with a discussion on open issues for research and future directions.
Open access
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education