Personal health data sharing is made possible by mobile and wearable technology. It has a tremendous and growing value for healthcare, helping both providers of care and medical research. The enhancement of engagement and collaboration within the healthcare industry depends on the secure and convenient sharing of personal health data. This chapter proposes an innovative user-centric health data sharing solution using a decentralized and permissioned blockchain to protect privacy using a channel formation scheme and enhanced identity management using the membership service. It is supported by the blockchain in response to the potential privacy issues and vulnerabilities existing in current personal health data storage and sharing systems as well as the concept of self-sovereign data ownership. Secure data sharing and collaboration in healthcare analytics are essential components to harness the power of data for informed decision-making and improved patient outcomes while maintaining patient privacy and data security. Achieving this delicate balance requires a combination of technological solutions, legal frameworks, and best practices to ensure that sensitive healthcare data is shared and analyzed in a secure and ethical manner as discussed in this chapter.
Artificial Intelligence in Healthcare and Education
INTRODUCTION: An analysis of the convergence of blockchain and artificial intelligence (AI) technology demonstrates how these technologies can work together to revolutionize data management across a wide range of industries with their synergistic potential. OBJECTIVES: This paper discusses the integration of blockchain and artificial intelligence, the authors present an innovative framework that takes advantage of their strengths. As a result of blockchain's immutability and transparency, data can be securely stored and shared within this framework, making it ideal for sectors such as healthcare, finance, and supply chain. METHODS: To begin with, the paper discusses blockchain and artificial intelligence individually, emphasizing their respective advantages in decentralized data storage and intelligent decision-making. Blockchain-AI convergence is inevitable as both deal with data and value. RESULTS: As a result, the research paper highlights how blockchain and AI technologies can be transformed into transformative technologies. CONCLUSION: Using the synergistic framework presented in this paper, data management can be made more secure, transparent, and intelligent, with implications that go beyond traditional industries into emerging fields like the Internet of Things (IoT) and smart cities.
Open access
Artificial Intelligence in Healthcare and Education
Neurological disorders are a significant health challenge globally, affecting millions of individuals and imposing a considerable economic burden on healthcare systems. Early and accurate diagnosis plays a crucial role in improving patient outcomes and managing these disorders effectively. This abstract presents a novel approach that combines blockchain technology with deep learning algorithms to enhance the detection of neurological disorders. The proposed system leverages the decentralized and transparent nature of blockchain to securely store and share medical data, enabling seamless collaboration among healthcare providers, researchers, and patients. This infrastructure ensures data integrity, privacy, and accessibility, addressing critical concerns in medical data management. Furthermore, the deep learning approach employs advanced neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze large-scale neurological data, including medical images, electroencephalograms (EEGs), and clinical records. By leveraging the power of deep learning, the system can automatically extract relevant features and patterns from complex neurological data, enabling accurate diagnosis and early detection of various disorders. The integration of blockchain and deep learning offers several advantages. Firstly, it facilitates secure and decentralized storage of medical data, ensuring patient privacy and data integrity. Secondly, it enables seamless data sharing and collaboration among multiple stakeholders, promoting knowledge exchange and enhancing research capabilities. Lastly, deep learning algorithms improve the accuracy and efficiency of neurological disorder detection, enabling timely interventions and personalized treatment plans. The proposed system holds great potential in revolutionizing the field of neurological disorder diagnosis and management. By leveraging the combined power of blockchain and deep learning, healthcare providers can enhance their diagnostic capabilities, leading to improved patient outcomes, reduced healthcare costs, and accelerated research advancements. However, further research and development are necessary to address technical challenges, scalability issues, and regulatory considerations to realize the full potential of this innovative approach.
The rapid expansion of artificial intelligence (AI) in healthcare has revolutionized diagnostic practices, enabling applications such as tumor detection in medical imaging, genomic analysis, and predictive risk modeling for early disease prevention. Despite these advancements, concerns about the opacity, trustworthiness, and auditability of AI systems remain significant barriers to clinical adoption. Medical practitioners, regulators, and patients increasingly demand systems that not only produce accurate results but also provide verifiable guarantees regarding the integrity and accountability of diagnostic processes. Blockchain technology, with its intrinsic features of decentralization, immutability, and consensus-driven validation, offers a promising solution to these concerns. This manuscript investigates the integration of blockchain-powered verifiable AI models for medical diagnosis. We present a comprehensive framework that leverages federated learning for decentralized training, blockchain for immutable storage and consensus validation, and zero-knowledge proofs for cryptographic verification of model outputs. The proposed system ensures transparent audit trails, enhances data integrity, protects patient privacy, and simplifies compliance with regulatory frameworks such as HIPAA and GDPR. Through simulated case studies in medical imaging and predictive diagnostics, we demonstrate that blockchain integration improves diagnostic verifiability, reduces susceptibility to adversarial manipulation, and fosters patient-centric trust. While slight computational latency is introduced, the trade-off is justified by significantly stronger guarantees of transparency, reproducibility, and ethical accountability. This research underscores the transformative role of blockchain in shaping the future of verifiable AI-driven healthcare, providing pathways toward more reliable, transparent, and equitable medical diagnostic ecosystems.
Blockchain and artificial intelligence (AI) are two of the most disruptive modern technologies. A blockchain is a distributed ledger capable of storing data in blocks. The data stored on a blockchain is immutable and secured with cryptography. The blockchain can guarantee data security without the involvement of a third party. The integration of it with AI to create distributed artificial intelligence (DAI) is increasingly being used in various areas of human activity. AI can increase the efficiency of blockchains by streamlining computations and processes, reducing the burden on miners and decreasing latency. The latter results in faster transactions and reduced carbon footprint of the blockchain technology. Blockchain can help develop explainable AI by means of accessing an immutable record of unstructured medical data and processes used by the system in its decision-making process. The purpose of this narrative review is to analyze the possibilities of using a decentralized AI in medicine.
Blockchain Technology Applications and Security
Machine Learning in Healthcare
Artificial Intelligence in Healthcare and Education
Electronic Health Records (EHRs) play a vital role in the healthcare domain for the patient survival system. They can include detailed information such as medical histories, medications, allergies, immunizations, vital signs, and more. It can help to reduce medical errors, improve patient safety, and increase efficiency in healthcare delivery. EHR approaches are proven to be an efficient and successful way of sharing patients’ personal health information. These kinds of highly sensitive information are vulnerable to privacy and security associated threats. As a result, new solutions must develop to meet the privacy and security concerns in health information systems. Blockchain technology has the potential to revolutionize the way electronic health records (EHRs) are stored, accessed, and utilized by healthcare providers. By utilizing a distributed ledger, blockchain technology can help ensure that data is immutable and secure from tampering. In this article, a Hyperledger consortium network has been developed for sharing health records with enhanced privacy and security. The attribute based access control (ABAC) mechanism is used for controlling access to electronic health records. The use of ABAC on the network provides EHRs with an extra layer of security and control, ensuring that only authorized users have access to sensitive data. By using attributes such as user identity, role, and health condition, it is possible to precisely control access to records on blockchain. Besides, a Gaussian naïve Bayes algorithm has been integrated with this consortium network for prediction of cardiovascular disease. The prediction of cardiovascular is difficult due to its correlated risk factors. This system is beneficial for both patients and physicians as it allows physicians to quickly identify high-risk patients and easily provide them with patient severity level using feature weight prediction algorithms. Dynamic emergency access control privileges are used for the emergency team and will be withdrawn once the emergency has been resolved, depending on the severity score. The system is implemented with the following medical datasets: the heart disease dataset, the Pima Indian diabetes dataset, the stroke prediction dataset, and the body fat prediction dataset. The above datasets are obtained from the Kaggle repository. This system evaluates system performance by simulating various operations using the Hyperledger Caliper benchmarking tool. The performance metrics such as latency, transaction rate, resource utilization, etc . are measured and compared with the benchmark.
Temporal Heterogeneous Networks (THNs) are evolving networks that characterize many real-world applications such as citation and events networks, recommender systems, and knowledge graphs. Forecasting THNs involves predicting future connections within a network that evolves over time and comprises diverse types of nodes and interactions with varying temporal dynamics. Although some Graph Neural Networks (GNNs) models have been successfully applied to forecast THNs, there is a lack of a general overview of how the message-passing computation could be extended to treat THNs. Moreover, most of the current solutions exhibit pitfalls in their training and evaluation strategies. Hence, in this work, we propose a graph deep learning framework for THN forecasting. Our framework decomposes the computation of a GNN layer into multiple components and introduces two different schemes to update embedding representations for THNs. This design allows the classification of existing solutions into special instances of our framework and highlights their potential limitations. We also extend the set of benchmarks for THNs by introducing two novel high-resolution temporal heterogeneous graph datasets derived from an emerging Web3 platform and a well-established e-commerce website. Overall, we conducted the first massive evaluation of THNs solutions over four temporal heterogeneous network datasets on two different future link prediction tasks using a fair newly introduced evaluation setting that considers the evolving nature of the data. Based on the limitations of existing solutions, we develop a new model that combines working techniques from previous models and leverages a new embedding update scheme. Experiments show the prediction power of our model compared to current solutions for link prediction in temporal graphs. Moreover, the experimental evaluation highlights the strengths and weaknesses of the different solutions and shows the effectiveness of our framework design.
This paper explores the potential of using ChatGPT, a state-of-the-art conversational AI system, to enhance engagement and understanding of cryptocurrency. We first provide a comprehensive review of the existing literature on both cryptocurrency and ChatGPT. We then describe the background of cryptocurrency and the capabilities of ChatGPT. We present our methodology for collecting and preprocessing a dataset of cryptocurrency-related conversations and fine-tuning ChatGPT using reinforcement learning. Our results demonstrate the effectiveness of ChatGPT in generating contextually appropriate responses to cryptocurrency-related queries, with potential applications in areas such as customer support and education. However, we also identify challenges and limitations associated with the deployment of ChatGPT in this domain, including the need for robust data privacy measures and addressing potential biases. Our findings suggest promising directions for future research in enhancing conversational engagement and understanding of cryptocurrency through ChatGPT
Open access
Artificial Intelligence in Healthcare and Education
Nowadays, empowered by mobile networks, electronic health (E-health) can bring more advanced medical services. Electronic health records (EHRs) with rich medical knowledge have gained increasing popularity for supporting E-health. Blockchain-based secure storage and access are novel trends for EHRs, but there are still following unresolved problems in this area. On one hand, specific medical tasks (e.g., COVID-19 diagnosis) require corresponding EHRs, but it lacks the medicine knowledge graph to help doctors or researchers find the EHRs on-demand in the massive distributed and encrypted medical data. On the other hand, as the documents include knowledge, EHRs with digital copyrights shared by medical institutions or patients need to be protected during sharing. To address these challenges, this paper proposes a blockchain and Non-Fungible Token (NFT) empowered on-demand medicine knowledge sharing architecture for EHRs. First, we propose a medicine knowledge graph construction scheme based on smart contracts and medical task knowledge relationships. Second, to provide on-demand EHRs sharing, we design the EHRs matching and clustering algorithms, regarding the dynamic importance and similarity of graph nodes. Third, we establish the interplanetary file address driven NFT minting mechanism for EHRs to protect digital copyrights. Finally, we conduct experiments in Ethereum using real medical datasets, which demonstrates the feasibility and efficiency of the proposed architecture. To our best knowledge, this work is the first to realize the medicine knowledge graph with copyright protection for EHRs.
Blockchain technology is poised to significantly help the healthcare sector with its high degree of security, privacy, confidentiality and decentralization. In this research, the authors describe how blockchain technology can be used to transform existing medical data systems, and also present a framework that can be used to implement it. The goal of this framework is to provide secure storage of electronic records by defining detailed access rules for users of the proposed framework. The proposed system aims to leverage individual medical details, reduce the rate of duplicate testing, reduce treatment delays, and provide patients with sufficient information to make better decisions. When a patient visits a hospital, the hospital creates a patient record and stores it in a decentralized application [6] (DApp). A DApp is a blockchain-based platform for storing electronic medical records. Doctors can update patient records, and patients can access their medical records from her DApp using public keys. EHRs enable hospitals and doctors to track the information they need to comply with insurance companies and federal regulations. EHR acts as a central data source base where doctors’ orders for laboratory tests, x-rays, and other tests are stored.
Mohamed Emish, Hari Kishore Chaparala, Zeyad Kelani, Sean D. Young
Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights. While wearable device data helps to monitor, detect, and predict diseases and health conditions, some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns. Moreover, wearable devices have been recently available as commercial products; thus large, diverse, and representative datasets are not available to most researchers. In this article, we propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers (e.g., researchers) to make wearable device data more available to healthcare researchers. To secure the data transactions in a privacy-preserving manner, we use a decentralized approach using Blockchain and Non-Fungible Tokens (NFTs). To ensure data originality and integrity with secure validation, our marketplace uses Trusted Execution Environments (TEE) in wearable devices to verify the correctness of health data. The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share. To ensure user participation, we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs. We also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits. If widely adopted, we believe that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.
Bocheng Ren, Laurence T. Yang, Qingchen Zhang, Jun Feng · 5 authors
The rapid development and gradual integration of artificial intelligence and the Internet of Things have brought unprecedented opportunities for radically changing healthcare and treatments. However, the burgeoning in intelligent healthcare systems is severely bounded by data privacy and the security of AI models. Meanwhile, the limited local data forces conventional AI models to face the predicament in achieving personalized healthcare. Hence, we propose a blockchain-powered tensor meta-learning-driven intelligent healthcare system with IoT assistance. IoT devices as light nodes upload the local shareable data to the edge server(full node) for model training and perform the local private data by non-tampered model downloaded via smart contract. The system can not only use blockchain technology to ensure the strong consistency of the healthcare model but also protect private data from being leaked. Especially, we develop a tensor meta-learning model named tensor-prototype graph network to achieve efficient modeling of heterogeneous healthcare data. Building on the tensors and graph network, the model is conducive to capturing the data distribution when there are few labeled data. To evaluate our proposed approach, we have conducted experiments on three classic databases. The results demonstrate that our approach is capable of effectively promoting the performance of intelligent healthcare.
Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions.
In decentralized finance (DeFi), lenders can offer flash loans to borrowers, i.e., loans that are only valid within a blockchain transaction and must be repaid with fees by the end of that transaction. Unlike normal loans, flash loans allow borrowers to borrow large assets without upfront collaterals deposits. Malicious adversaries use flash loans to gather large assets to exploit vulnerable DeFi protocols. In this paper, we introduce a new framework for automated synthesis of adversarial transactions that exploit DeFi protocols using flash loans. To bypass the complexity of a DeFi protocol, we propose a new technique to approximate the DeFi protocol functional behaviors using numerical methods (polynomial linear regression and nearest-neighbor interpolation). We then construct an optimization query using the approximated functions of the DeFi protocol to find an adversarial attack constituted of a sequence of functions invocations with optimal parameters that gives the maximum profit. To improve the accuracy of the approximation, we propose a novel counterexample driven approximation refinement technique. We implement our framework in a tool named FlashSyn. We evaluate FlashSyn on 16 DeFi protocols that were victims to flash loan attacks and 2 DeFi protocols from Damn Vulnerable DeFi challenges. FlashSyn automatically synthesizes an adversarial attack for 16 of the 18 benchmarks. Among the 16 successful cases, FlashSyn identifies attack vectors yielding higher profits than those employed by historical hackers in 3 cases, and also discovers multiple distinct attack vectors in 10 cases, demonstrating its effectiveness in finding possible flash loan attacks.
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.
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.
Eric Appiah Mantey, Conghua Zhou, S. Srividhya, Sanjiv Jain · 5 authors
Blockchain is a recent revolutionary technology primarily associated with cryptocurrencies. It has many unique features including its acting as a decentralized, immutable, shared, and distributed ledger. Blockchain can store all types of data with better security. It avoids third-party intervention to ensure better security of the data. Deep learning is another booming field that is mostly used in computer applications. This work proposes an integrated environment of a blockchain-deep learning environment for analyzing the Electronic Health Records (EHR). The EHR is the medical documentation of a patient which can be shared among hospitals and other public health organizations. The proposed work enables a deep learning algorithm act as an agent to analyze the EHR data which is stored in the blockchain. This proposed integrated environment can alert the patients by means of a reminder for consultation, diet chart, etc. This work utilizes the deep learning approach to analyze the EHR, after which an alert will be sent to the patient's registered mobile number.
Deep Rahul Shah, Dev Ajay Dhawan, Samit Nikesh Shah, Pannag Rajesh Shah · 5 authors
In the information innovation unrest, electronic clinical records are a standard method for putting away patients' data in emergency clinics. Albeit some emergency clinic frameworks utilize server-based patient detail the board frameworks, they need a lot of capacity to store every one of the patients' clinical reports, in this manner influencing the versatility. Simultaneously, they are confronting a few troubles, for example, interoperability concerns, security and protection issues, digital as-saults to the concentrated stockpiling, and keeping up with sticking to clinical approaches. The proposed model is a private blockchain-based patient detail the board framework as most would consider to be normal to resolve the above issues. Arrangement proposes an appropriate secure record to grants effective framework access and frameworks recovery, which is secure and unchanging and man-made conscious’s fuelled instruments for clinical choice help. A better consensus system accomplishes the consensus of the information without huge energy use and organization congestion. Also, our model accomplishes high information security standards given a combination of crossbreed access control components, public-key cryptography, and a protected live ailment checking instrument. The proposed arrangement brings about effectively conveyed shrewd contracts according to the jobs of the framework, Ethereum based accreditation age and approval, Post Recovery Co-Morbidity Prediction, Disease Prediction Based on Symptoms, and Drug Recommendation gave Side Effects. The general goal of this arrangement is to bring the whole clinical industry into a common stage by utilizing a decentralized way to deal with a store, share clinical subtleties while disposing of the need to keep up with printed clinical records, and backing specialists utilizing exceptionally precise machine learning tools
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.