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.
Data and technology have made it possible to find solutions to a wide variety of challenges in healthcare. Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. It has been demonstrated that machine learning may be useful in a variety of fields because of its ability to recognize patterns in data, perform analyses, and reach choices. To create appropriate choices, machine learning requires a sufficient amount of data. Data sharing and data reliability are critical components of machine learning in order to increase its accuracy. Blockchain Technology’s decentralized database places a premium on data exchange. Consensus in Blockchain technology ensures the legitimacy and security of data. Converging these two technologies can result in highly accurate machine learning results combined with the security and stability of Blockchain Technology. In this paper, we will have opportunity to know about Machine learning integration along with Blockchain in the field of Healthcare. We proposed secure, transparent and intelligent methods in the Smart Health care Industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.
Healthcare systems based on the Internet of Things have an increasing demand for health sensing technology. To manage the data collected and sampled by medical devices, traditional centralized data management will lead to attacks such as single point of failure, which pose a security threat. Aiming at the problems of low data trust and uncontrolled data sharing in telemedicine, we proposed blockchain-based secure medical data management and disease prediction. To securely manage healthcare data, we carefully designed three-tier architecture. Specifically, in the user sensor layer, medical sensors monitor the patient status in real-time. In the storage layer, to protect the privacy of patients, we stored their data in blocks and quantified the medical data by using information entropy technology. In addition, in the blockchain layer, we also used smart contracts for application, authorization, and access control of health data to eliminate privacy leaks caused by internal and external security risks. The information summary is recorded on the blockchain to ensure the integrity of backtracking and anti-repudiation. We designed an extensible machine learning algorithm to predict disease types using a disease prediction model algorithm based on transfer learning. Security analysis and numerical results showed that the proposed scheme can effectively manage the safety data of telemedicine and predict the patient's future condition.
G. Ramesh, Avinash Sharma, D. V. Lalitha Parameswari, Ch. Mallikarjuna Rao · 5 authors
Biomedical databases or repositories have scientific information that is evidence based and protecting such documents from tampering or non-repudiation is very significant. The traditional techniques for the same have limitations in the distributed environments. Scientific contributions are to be safeguarded and it is one of the challenging problems. Blockchain is the promising technology that can support distributed ledger of transactions and thus it is found suitable for protecting biomedical repositories. As blockchain is a proven technology associated with crypto-currency known as Bitcoin in finance domain, it has plenty of opportunities in other domains. In this paper, a framework that is based on blockchain technology (BCT) for protection of biomedical databases with integrity and non-repudiation is presented. The framework will have underlying mechanisms to exploit blockchain to have a protection service and smart contracts to be more flexible and dynamic to adapt new requirements from time to time. The framework is domain specific but can pave way for motivation for adapting it to new domains as well.
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
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
Medical Cyber-Physical Systems support the mobility of electronic health records data for clinical research to accelerate new scientific discoveries. Artificial Intelligence improves medical informatics, but current centralized data training and insecure data storage management techniques expose private medical data to unauthorized foreign entities. In this paper, a Federated Learning-based Electronic Health Record sharing scheme is proposed for Medical Informatics to preserve patient data privacy. A decentralized Federated Learning-based Convolutional Neural Network model trains data locally in the hospital and stores results in a private InterPlanetary File System. A secondary global model is trained at the research center using the local models. Private IPFS secures all medical data stored locally in the hospital. The novelty of this study resides in securing valuable hospital biomedical data useful for clinical research organizations. Blockchain and smart contracts enable patients to negotiate with external entities for rewards in exchange for their data. Evaluation results demonstrate that the decentralized CNN model performs better in accuracy, sensitivity, and specificity, similar to the traditional centralized model. The performance of the Private IPFS exceeds the Blockchain-based IPFS based on file upload and download time. The scheme is suitable for promoting a secure and privacy-friendly environment for sharing data with clinical research centers for biomedical research.
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
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
Due to recent development in technology, major changes have been noticed in human being’s life. Today’s lives of human being are becoming more convenient (i.e., in terms of living standard). In current real-world’s applications, we have shifted our attention from wired devices to wireless devices. In result, we moved into the era of smart technology, where many internet devices are interconnected in a distributed and decentralized way. Such internet connected devices (ICDs) or internet of things (IoTs) are generating a lot of data (i.e., via communicating other smart devices). With the tremendous increase in the amount of data, there is a higher requirement to process this huge amount of data (generated through billion of ICDs) using efficient ML algorithms. In the past decade, we refer data mining (DM) algorithms to make some decision from collected data-sets. But, due to increasing data on a large scale, DM fail to handle this data. So, as substitute of DM algorithms and to refine this information in an efficient manner, we require tradition analytics algorithms, i.e., ML or DM algorithms. In current scenario, some of the ML algorithms (available to analysis this data) are supervised (used with labeled data), unsupervised (used with unlabeled data) and semi-supervised (work as reward-based learning). Supervised learning algorithms are like Linear Regression, Classification, and k-nearest neighbor 150(KNN), etc. Whereas, unsupervised learning algorithms are clustering, k-means, etc. In general, ML focuses on building the systems that learn and hence improves with the knowledge and experience. Being the heart of artificial intelligence (AI) and data science, ML is gaining popularity day by day. Notice that a sub-set of AI is ML. Several algorithms have already been developed (in the past decade) for processing of data, although this field focuses on developing new learning algorithm for big data computability with minimum complexity (i.e., in terms of time and space). ML algorithms are not only applicable to computer science field but also extend to medical, psychological, marketing, manufacturing, automobile, etc. On another side, Big Data including Deep learning are the two primary and highly demandable fields of data science. Here, Deep learning is a subset of ML, also a part of computer vision or AI. The large (or massive) amount of data related to a specific domain which forms Big Data (in form of 5 Vs like Velocity, Volume, Value, Variety, and Veracity), contains valuable information related to various fields like marketing, automobile, finance, cyber security, medical, fraud detection, etc. Such real-world’s applications are creating a lot of information every day. The valuable (i.e., needful, or meaningful) information required to be processed (or retrieved) from analysis of this unstructured/large amount of data for further processing of the data for future use (or for prediction). Big organizations have to deal with the large amount of data for prediction, classification, decision making, etc. The use of ML algorithms for big data analytics (DA) includes deep learning, which extracts the high-level semantics from the valuable (meaningful) information form the data. It uses hierarchical process for efficient processing and retrieving the complex abstraction from the data. Hence, this chapter discusses several algorithms of ML, to analysis Big Data. The AI subset, including ML algorithms, is also, Deep learning algorithms is being discussed here (i.e., analyzing this Big Data for accurate prediction). Later, this chapter focuses on the benefits of ML, deep learning algorithms in analyzing the large amount of data (i.e., in unsupervised or unstructured form) for numerous complex problems like information retrieval, medical diagnosis, cognitive science, indexing using semantic analysis, data tagging, speech recognition, natural language processing (NLP), etc. Also, weakness, raised issues, and challenges (during analysis big data) using (in) ML or deep learning have been discussed in detail. In other words, research gaps in using ML, deep learning algorithms for big data will also be discussed (with covering future research directions/trends). In last, the importance of smart era, computational intelligence, AI has been discussed in this chapter in detail.
K. Renuka Devi, S. Suganyadevi, S Karthik, N Ilayaraja
In the modern world, there is a purpose of storing, retrieving and analysing huge volumes of data. So, big data becomes one of the reliable aspects to analyse the large volumes of data to come to a better decision. Usually, big data is used to analyse large data set of information which cannot be handled by the traditional data processing software. This includes both structured and unstructured data and analyses the hidden patterns, correlations to retrieve the accurate results which would be helpful to improve the market results in organizations. So, securing the big data is one of the tedious processes in this real world in order to guard the data from attacks, theft or other malicious activities to maintain the confidentiality. The main characteristics of big data include Volume, Velocity, Variety, Veracity and Value. This paper mainly focused on several challenges in big data faced by healthcare domain and the technologies to maintain the security in it.
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
Sin Kit Lo, Yue Liu, Qinghua Lu, Chen Wang · 7 authors
Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organizations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multistakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. Additionally, we propose a weighted fair data sampler algorithm to enhance fairness in training data. We evaluate the proposed approach using a COVID-19 X-ray detection use case. The evaluation results show that the approach is feasible to enable accountability and improve fairness. The proposed algorithm can achieve better performance than the default federated learning setting in terms of the model’s generalization and accuracy.
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
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