Blockchain Papers

Hoda Hamouda, Victoria L. Lemieux

The decentralized applications and distributed ledgers of the blockchain technology (BT) make the exchange of health records more secure, allow users to be the primary owners of their health records, and provide higher protection of users’ records due to an ability to exchange information without revealing their identifiable information. The paper discusses what human-centered design (HCD) methods revealed about the user experience of individuals interacting with a BT-based solution that lets users contribute their de-identified health data to research projects in precision medicine. The methods revealed challenges in the user experience and presented the solutions carried out throughout the iteration phases of the solution’s user experience. Despite the privacy-preserving benefits of blockchain-based platforms, the complicated architecture of the technology and management of BT wallets constitute a real challenge to designing a user-friendly experience. This negatively impacts the adoption and implementation of BT-based solutions in health records management.

Caspar Barnes, Mateo Aboy, Timo Minssen, Jemima Winifred Allen · 7 authors

Participation in research is supposed to be voluntary and informed. Yet it is difficult to ensure people are adequately informed about the potential uses of their biological materials when they donate samples for future research. We propose a novel consent framework which we call "demonstrated consent" that leverages blockchain technology and generative AI to address this problem. In a demonstrated consent model, each donated sample is associated with a unique non-fungible token (NFT) on a blockchain, which records in its metadata information about the planned and past uses of the sample in research, and is updated with each use of the sample. This information is accessible to a large language model (LLM) customized to present this information in an understandable and interactive manner. Thus, our model uses blockchain and generative AI technologies to track, make available, and explain information regarding planned and past uses of donated samples.

Yazan Otoum, C. Hu, Eyad Haj Said, Amiya Nayak

Federated learning offers a framework for developing local models across institutions while safeguarding sensitive data. This paper introduces a novel approach for heart disease prediction using the TabNet model, which combines the strengths of tree-based models and deep neural networks. Our study utilizes the Comprehensive Heart Disease and UCI Heart Disease datasets, leveraging TabNet’s architecture to enhance data handling in federated environments. Horizontal federated learning was implemented using the federated averaging algorithm to securely aggregate model updates across participants. Blockchain technology was integrated to enhance transparency and accountability, with smart contracts automating governance. The experimental results demonstrate that TabNet achieved the highest balanced metrics score of 1.594 after 50 epochs, with an accuracy of 0.822 and an epsilon value of 6.855, effectively balancing privacy and performance. The model also demonstrated strong accuracy with only 10 iterations on aggregated data, highlighting the benefits of multi-source data integration. This work presents a scalable, privacy-preserving solution for heart disease prediction, combining TabNet and blockchain to address key healthcare challenges while ensuring data integrity.

Nitin Rathore, Aparna Kumari, Margi Patel, Alok Sinh Chudasama · 7 authors

ABSTRACT In this article, we delve into the intersection of blockchain technology and artificial intelligence (AI) to fortify the security of electronic health records (EHRs). Existing EHR systems face challenges like interoperability issues, user interface complexities, and data security concerns, hindering seamless patient care and healthcare delivery. This article thoroughly explores the hurdles associated with EHR security and investigates the potential of employing blockchain and AI solutions to mitigate these challenges. The review underscores the necessity for resilient, interoperable systems to protect sensitive health information by pinpointing opportunities for collaborative strategies. In essence, this article contributes valuable perspectives on the dynamic landscape of EHR security, guiding future research and development at the crossroads of blockchain; we presented a case study on EHR security, customized care, predictive analytics, and more efficient healthcare delivery. Using case studies as illustrative examples, the article scrutinizes practical applications, shedding light on successful implementations and areas requiring refinement. Several outcomes for the predictive analytics of patients' surgeries are shown in this case study. Next, we compare blockchain‐based EHR systems with existing, non‐blockchain‐based EHR systems. Finally, we present the concluding remarks with future directions for integrating blockchain, AI, and EHR systems.

Archana Bathula, Suneet Kumar Gupta, Suresh Merugu, Luca Saba · 11 authors

Abstract The fusion of blockchain and artificial intelligence (AI) marks a paradigm shift in healthcare, addressing critical challenges in securing electronic health records (EHRs), ensuring data privacy, and facilitating secure data transmission. This study provides a comprehensive analysis of the adoption of blockchain and AI within healthcare, spotlighting their role in fortifying security and transparency leading the trajectory for a promising future in the realm of healthcare. Our study, employing the PRISMA model, scrutinized 402 relevant articles, employing a narrative analysis to explore the fusion of blockchain and AI in healthcare. The review includes the architecture of AI and blockchain, examines AI applications with and without blockchain integration, and elucidates the interdependency between AI and blockchain. The major findings include: (i) it protects data transfer, and digital records, and provides security; (ii) enhances EHR security and COVID-19 data transmission, thereby bolstering healthcare efficiency and reliability through precise assessment metrics; (iii) addresses challenges like data security, privacy, and decentralized computing, forming a robust tripod. The fusion of blockchain and AI revolutionize healthcare by securing EHRs, and enhancing privacy, and security. Private blockchain adoption reflects the sector’s commitment to data security, leading to improved efficiency and accessibility. This convergence promises enhanced disease identification, response, and overall healthcare efficacy, and addresses key sector challenges. Further exploration of advanced AI features integrated with blockchain promises to enhance outcomes, shaping the future of global healthcare delivery with guaranteed data security, privacy, and innovation.

Tiago Nunes, Paulo Rupino da Cunha, João Mendes de Abreu, J. Duarte · 5 authors

Amid global health challenges, resilient health systems require continuous innovation and progress. Stakeholders highlight the critical role of digital technologies in accelerating this progress. However, the digital health field faces significant challenges, including the sensitivity of health data, the absence of evidence-based standards, data governance issues, and a lack of evidence on the impact of digital health strategies. Overcoming these challenges is crucial to unlocking the full potential of digital health innovations in enhancing healthcare delivery and outcomes. Prioritizing security and privacy is essential in developing digital health solutions that are transparent, accessible, and effective. Non-fungible tokens (NFTs) have gained widespread attention, including in healthcare, offering innovative solutions and addressing challenges through blockchain technology. This paper addresses the gap in systematic-level studies on NFT applications in healthcare, aiming to comprehensively analyze use cases and associated research challenges. The search included primary studies published between 2014 and November 2023, searching in a balanced set of databases compiling articles from different fields. A review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and strictly focusing on research articles related to NFT applications in the healthcare sector. The electronic search retrieved 1902 articles, ultimately resulting in 15 articles for data extraction. These articles span applications of NFTs in medical devices, pathology exams, diagnosis, pharmaceuticals, and other healthcare domains, highlighting their potential to eliminate centralized trust sources in health informatics. The review emphasizes the adaptability and versatility of NFT-based solutions, indicating their broader applicability across various healthcare stages and expansion into diverse industries. Given their role in addressing challenges associated with enhancing data integrity, availability, non-repudiation, and authentication, NFTs remain a promising avenue for future research within digital health solutions.

Hossein Omidian

No abstract is available for this record.

Haoxiang Luo, Jian Luo, Athanasios V. Vasilakos

No abstract is available for this record.

Bhupinder Singh, Christian Kaunert

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.

Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal

Federated learning (FL) represents an advanced approach to tackling the issues linked with training machine learning (ML) models using distributed data while upholding privacy and security. It functions by enabling collaborative model training across a network of edge devices or servers, all without the need to transfer raw data. In place of sending data to a central server, which could potentially compromise privacy, federated learning empowers individual devices to conduct local training on their respective data. These updates are subsequently combined to develop an enhanced global model over multiple iteration. Additionally, as artificial intelligence (AI) becomes pervasive in novel application areas, concerns about the privacy of data and users are on the rise. This article offers an in-depth analysis of the advancements in FL, covering a wide array of topics including methodologies, applications, and challenges. By sidestepping the need to transfer raw data and instead focusing on sharing model updates or gradients, FL ensures the preservation of privacy and the efficient utilization of resources. Additionally, we investigate the diverse spectrum of application domains where FL holds significance. Instances encompass healthcare, finance, agriculture, education, Internet of Things (IoT), and industrial processes, all benefiting from the capacity of federated learning to harness data from decentralized sources without compromising data security. This article addresses complications such as model diversity, Non-IID (independent and identically distributed) data distribution, communication complexities, and security vulnerabilities. Furthermore, we discuss considerations related to regulatory compliance and ethics within the context of federated learning, particularly as data privacy regulations intensify.

Khulekani Sibanda, Patrick Ndayizigamiye, Hossana Twinomurinzi

Introduction: In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review. Methods: This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production. Results: Analysis of 19 selected papers revealed three primary use cases: patient-centric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access. Conclusion: NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions.

Wail Zellagui, Abdessamad Imine, Yamina Tadjeddine

Recent advances in the field of large language models (LLMs), particularly the ChatGPT family, have given rise to a powerful and versatile machine interlocutor, packed with knowledge and challenging our understanding of learning. This interlocutor is a double-edged sword: it can be harnessed for a wide variety of beneficial tasks, but it can also be used to cause harm. This study explores the complicated interaction between ChatGPT and the growing problem of cryptocurrency fraud. Although ChatGPT is known for its adaptability and ethical considerations when used for harmful purposes, we highlight the deep connection that may exist between ChatGPT and fraudulent actions in the volatile cryptocurrency ecosystem. Based on our categorization of cryptocurrency frauds, we show how to influence outputs, bypass ethical terms, and achieve specific fraud goals by manipulating ChatGPT prompts. Furthermore, our findings emphasize the importance of realizing that ChatGPT could be a valuable instructor even for novice fraudsters, as well as understanding and safely deploying complex language models, particularly in the context of cryptocurrency frauds. Finally, our study underlines the importance of using LLMs responsibly and ethically in the digital currency sector, identifying potential risks and resolving ethical issues. It should be noted that our work is not intended to encourage and promote fraud, but rather to raise awareness of the risks of fraud associated with the use of ChatGPT.

Raushan Myrzashova, Saeed Hamood Alsamhi, Ammar Hawbani, Edward Curry · 6 authors

Medical healthcare centers are envisioned as a promising paradigm to handle vast data for various disease diagnoses using artificial intelligence. Traditional Machine Learning algorithms have been used for years, putting the sensitivity of patients' medical data privacy at risk. Collaborative data training, where multiple hospitals (nodes) train and share encrypted federated models, solves the issue of data leakage and unites resources of small and large hospitals from distant areas. This study introduces an innovative framework that leverages blockchain-based Federated Learning to identify 15 distinct lung diseases, ensuring the preservation of privacy and security. The proposed model has been trained on the NIH Chest Ray dataset (112 120 X-Ray images), tested, and evaluated, achieving test accuracy of 92.86%, a latency of 43.518625 ms, and a throughput of 10034017 bytes/s. Furthermore, we expose our framework blockchain to stringent empirical tests against leading cyber threats to evaluate its robustness. With resilience metrics consistently nearing 87% against three evaluated cyberattacks, the proposed framework demonstrates significant robustness and potential for healthcare applications. To the best of our knowledge, this is the first paper on the practical implementation of blockchain-empowered FL with such data and several diseases, including multiple disease coexistence detection.

Isabirye Edward Kezron

The rapid advancement and integration of Artificial Intelligence (AI) across critical sectors — including healthcare, finance, defense, and infrastructure — have exposed an often-overlooked risk: vulnerabilities within the AI supply chain. This research examines the security challenges and potential threats affecting AI model development and deployment, focusing on adversarial attacks, data poisoning, model theft, and compromised third-party components. By dissecting the AI supply chain into its core stages — data sourcing, model training, deployment, and maintenance — this study identifies key entry points for malicious actors. The paper proposes a multi-layered security framework combining blockchain-based data provenance, federated learning for decentralized model training, and zero-trust architecture to ensure secure deployment. Additionally, it explores how adversarial training, model watermarking, and real-time anomaly detection can mitigate risks without sacrificing model performance. Case studies of high-profile AI breaches are analyzed to demonstrate the consequences of unsecured pipelines, emphasizing the urgency of securing AI systems.

Giuseppe Rovere, Francesco Bosco, Angelo Miceli, Salvatore Ratano · 10 authors

Blockchain technology has gained popularity since the invention of Bitcoin in 2008. It offers a decentralized and secure system for managing and protecting data. In the healthcare sector, where data protection and patient privacy are crucial, blockchain has the potential to revolutionize various aspects, including patient data management, orthopedic registries, medical imaging, research data, and the integration of Internet of Things (IoT) devices. This manuscript explores the applications of blockchain in orthopedics and highlights its benefits. Furthermore, the combination of blockchain with artificial intelligence (AI), machine learning, and deep learning can enable more accurate diagnoses and treatment recommendations. AI algorithms can learn from large datasets stored on the blockchain, leading to advancements in automated clinical decision-making. Overall, blockchain technology has the potential to enhance data security, interoperability, and collaboration in orthopedics. While there are challenges to overcome, such as adoption barriers and data sharing willingness, the benefits offered by blockchain make it a promising innovation for the field.

Mansi Gupta, Mohit Kumar, Yash Gupta

No abstract is available for this record.

Stephan Rau, Alexander Rau, Johanna Nattenmüller, Anna Maria Fink · 7 authors

BACKGROUND: We investigated the potential of an imaging-aware GPT-4-based chatbot in providing diagnoses based on imaging descriptions of abdominal pathologies. METHODS: Utilizing zero-shot learning via the LlamaIndex framework, GPT-4 was enhanced using the 96 documents from the Radiographics Top 10 Reading List on gastrointestinal imaging, creating a gastrointestinal imaging-aware chatbot (GIA-CB). To assess its diagnostic capability, 50 cases on a variety of abdominal pathologies were created, comprising radiological findings in fluoroscopy, MRI, and CT. We compared the GIA-CB to the generic GPT-4 chatbot (g-CB) in providing the primary and 2 additional differential diagnoses, using interpretations from senior-level radiologists as ground truth. The trustworthiness of the GIA-CB was evaluated by investigating the source documents as provided by the knowledge-retrieval mechanism. Mann-Whitney U test was employed. RESULTS: The GIA-CB demonstrated a high capability to identify the most appropriate differential diagnosis in 39/50 cases (78%), significantly surpassing the g-CB in 27/50 cases (54%) (p = 0.006). Notably, the GIA-CB offered the primary differential in the top 3 differential diagnoses in 45/50 cases (90%) versus g-CB with 37/50 cases (74%) (p = 0.022) and always with appropriate explanations. The median response time was 29.8 s for GIA-CB and 15.7 s for g-CB, and the mean cost per case was $0.15 and $0.02, respectively. CONCLUSIONS: The GIA-CB not only provided an accurate diagnosis for gastrointestinal pathologies, but also direct access to source documents, providing insight into the decision-making process, a step towards trustworthy and explainable AI. Integrating context-specific data into AI models can support evidence-based clinical decision-making. RELEVANCE STATEMENT: A context-aware GPT-4 chatbot demonstrates high accuracy in providing differential diagnoses based on imaging descriptions, surpassing the generic GPT-4. It provided formulated rationale and source excerpts supporting the diagnoses, thus enhancing trustworthy decision-support. KEY POINTS: • Knowledge retrieval enhances differential diagnoses in a gastrointestinal imaging-aware chatbot (GIA-CB). • GIA-CB outperformed the generic counterpart, providing formulated rationale and source excerpts. • GIA-CB has the potential to pave the way for AI-assisted decision support systems.

Deepika Khedekar

No abstract is available for this record.

Jason P. Rothberg, Colin Keogh, Yury Rusinovich

Xavatar is a media, educational, and therapeutic platform specializing in immersive virtual reality (VR) and augmented reality (AR) content, as well as seamless interconnectivity across various devices such as mobiles, tablets, and computers. It is aimed at improving the lives of patients with chronic mobility and communication disorders, including dementia, Alzheimer's, autism, chronic immobility, isolation, and long-term hospitalization. The project represents a fusion of digital technologies including the Metaverse, artificial intelligence (AI), and Web3, all designed to enhance healthcare interactions and patient support. This opinion piece explores the transformative potential of Xavatar, highlighting its role in shaping future healthcare landscapes through innovative, empathetic, and engaging digital solutions.

Rajesh Kumar, Yong Chen, Zhi-Shuang Gong, Zaid Al‐Huda · 5 authors

Healthcare institutions, including hospitals, clinics, and medical imaging centers, often encounter difficulties in sharing medical images across different systems and facilities. Further, most healthcare systems face concerns related to data security and patient privacy. This paper is based on the development of a blockchain-powered medical image storage and sharing platform. The platform’s architecture includes components such as smart contracts, encryption mechanisms, and decentralized storage systems, which collectively enable seamless and trustworthy medical image sharing. The use of smart contracts provides a reliable framework for access control and data sharing permissions. The encryption mechanisms safeguard sensitive patient information during transmission and storage, bolstering data security and privacy. Additionally, the utilization of decentralized storage systems ensures redundant and distributed data storage, mitigating the risk of data loss, manipulation, or security threats. The research underlying this project involves leveraging blockchain’s inherent properties of decentralization, immutability, and transparency to establish a secure and interoperable infrastructure for medical image exchange. By harnessing the potential of distributed ledger technology, the proposed platform addresses the existing challenges of fragmented systems, limited interoperability, and data silos in medical image sharing. Blockchain technology addresses critical challenges in medical image sharing, paving the way for enhanced collaboration, improved patient care, and increased efficiency in healthcare.

Rachna Rana, Pankaj Bhambri

Blockchain is forward-looking knowledge that may be used to dispense imaginative explanations athwart an assortment of activities involving healthcare. The COVID-19 pandemic has meaningfully affected healthcare on a universal gauge and has hastened the implementation of digital technology. The immutability, decentralization, and transparency of one of these new digital technologies, blockchain, make it particularly valuable in a mix of fields, including the control of access to and maintenance of medical information and mobile health. The chapter thoroughly analyzes the blockchain applications in the healthcare industry, both those relevant to COVID-19 and those not. Sickness control and reconnaissance, the scrutinizing of imperviousness or vaccine permits, and communication tracing were the main COVID-19-associated solicitations described. Management of electronic medical records, the Internet, and social media were the top three non-COVID-19 solicitations monitoring the supply chain and the Internet of Things (for instance inaccessible observing or transportable condition). Nine (2%) educations specified practical medical use and agreement, while 277 (66%) of the 415 reports described the technical performance of blockchain prototype platforms. The remaining investigations (129 [31%] of 415) were all strictly technical in nature. Ethereum and Hyperledger were the most frequently utilized platforms. A blockchain network helps realm and conversation persistent information in the healthcare business. Blockchain science has an opportunity to precisely recognize medical errors that range from hilarious to horrible. Bitcoin has implications for solving trial deception and improving the experiences of patients.

Xuetao Pu, Rong Jiang, Zhiming Song, Zhihong Liang · 5 authors

The rapid development of the Hospital Information System has significantly enhanced the convenience of medical research and the management of medical information. However, the internal misuse and privacy leakage of medical big data are critical issues that need to be addressed in the process of medical research and information management. Access control serves as a method to prevent data misuse and privacy leakage. Nevertheless, traditional access control methods, limited by their single usage scenario and susceptibility to single point failures, fail to adapt to the polymorphic, real-time, and sensitive characteristics of medical big data scenarios. This paper proposes a smart contracts and risk-based access control model (SCR-BAC). This model integrates smart contracts with traditional risk-based access control and deploys risk-based access control policies in the form of smart contracts into the blockchain, thereby ensuring the protection of medical data. The model categorizes risk into historical and current risk, quantifies the historical risk based on the time decay factor and the doctor's historical behavior, and updates the doctor's composite risk value in real time. The access control policy, based on the comprehensive risk, is deployed into the blockchain in the form of a smart contract. The distributed nature of the blockchain is utilized to automatically enforce access control, thereby resolving the issue of single point failures. Simulation experiments demonstrate that the access control model proposed in this paper effectively curbs the access behavior of malicious doctors to a certain extent and imposes a limiting effect on the internal abuse and privacy leakage of medical big data.

Jyotiraditya Gandhi, Krishnakumar Maurya, Viveksingh Panwar, Utpalkumar Patel · 6 authors

Abstract: In recent years, the integration of blockchain technology with healthcare systems has garnered considerable attention due to its potential to enhance security, privacy, and interoperability in managing patient medical records. This paper proposes a novel approach to patient medical record management by leveraging Ethereum blockchain and InterPlanetary File System (IPFS) for storage, within a government-controlled framework. The system ensures secure and immutable storage of patients' medical records, accessible only by verified medical professionals, thus facilitating informed diagnosis and treatment. Additionally, the platform provides mechanisms for patient recourse in case of inaccuracies, as well as potential integration with insurance agencies. Furthermore, the proposed system envisages a future extension to facilitate anonymized data sharing with the scientific community, thereby contributing to advancements in medical research. This paper provides a comprehensive academic description of the proposed approach, discussing its technical architecture, security measures, regulatory framework, and potential impact on healthcare delivery and research.

Junsheng Mu, Michel Kadoch, Tongtong Yuan, Wenzhe Lv · 6 authors

Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhead and data heterogeneity. This paper proposes novel FL techniques using explainable artificial intelligence (XAI) for efficient, accurate, and trustworthy analysis. A heterogeneity-aware causal learning approach selectively sparsifies model weights based on their causal contributions, significantly reducing communication requirements while retaining performance and improving interpretability. Furthermore, blockchain provides decentralized quality assessment of client datasets. The assessment scores adjust aggregation weights so higher-quality data has more influence during training, improving model generalization. Comprehensive experiments show our XAI-integrated FL framework enhances efficiency, accuracy and interpretability. The causal learning method decreases communication overhead while maintaining segmentation accuracy. The blockchain-based data valuation mitigates issues from low-quality local datasets. Our framework provides essential model explanations and trust mechanisms, making FL viable for clinical adoption in medical image analysis.