Blockchain Papers

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.

Krishan Kansal

Abstract: In the era of digital innovation, Artificial Intelligence (AI) has emerged as a pivotal catalyst, unlocking new avenues for the evolution of Web 3.0. Web 3.0 signifies the next phase of the internet, characterized by decentralized structures, peer-topeer networks, and cutting-edge technologies like blockchain and smart contracts. This research provides an in-depth exploration of the role played by AI in shaping Web 3.0, delving into both its opportunities and challenges. AI proves instrumental in processing and analysing vast datasets with increased efficiency, fostering intelligent decision-making and insightful outcomes. The paper extensively covers essential Web 3.0 concepts and technologies, encompassing the Semantic Web and ontologies, and underscores AI's transformative potential across diverse industries such as healthcare, finance, and education. An analysis of the challenges posed by AI in the Web 3.0 landscape, including issues of data privacy, bias, trust, and ethics, is presented. Furthermore, the research examines the broader societal implications of AI in Web 3.0. Conclusively, the paper outlines future directions and implications of AI within the Web 3.0 framework, proposing potential areas for subsequent research. By contributing to a comprehensive understanding of AI's impact on web development and its broader societal implications, this research aims to guide future endeavours in this dynamic field.

Tarandeep Kaur Bhatia, Prerana, Sudhanshu Singh, Navya Saluja · 5 authors

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.

Naresh Kshetri, Rahul Mishra, Mir Mehedi Rahman, Tanja Steigner

In the ever-evolving healthcare sector, the widespread adoption of Internet of Things and wearable technologies facilitates remote patient monitoring. However, the existing client/server infrastructure poses significant security and privacy challenges, necessitating strict adherence to healthcare data regulations. To combat these issues, a decentralized approach is imperative, and blockchain technology emerges as a compelling solution for strengthening Internet of Things and medical systems security. This paper introduces HNMblock, a model that elevates the realms of epidemiological monitoring, medical system security, and wellness enhancement. By harnessing the transparency and immutability inherent in blockchain, HNMblock empowers real-time, tamper-proof tracking of epidemiological data, enabling swift responses to disease outbreaks. Furthermore, it fortifies the security of medical systems through advanced cryptographic techniques and smart contracts, with a paramount focus on safeguarding patient privacy. HNMblock also fosters personalized health care, encouraging patient involvement and data-informed decision-making. The integration of blockchain within the healthcare domain, as exemplified by HNMblock, holds the potential to revolutionize data management, epidemiological surveillance, and wellness, as meticulously explored in this research article.

Sumit Oberoi, Sugandh Arora, Balraj Verma, Krishna Kanta Roy

This study aims to identify artificial intelligence and blockchain technology's publication productivity and intellectual structure in the healthcare industry. This study employs a bibliometric-content analysis technique to determine intellectual structure and publication productivity. The Scopus database analyses identified research articles from 2018 to 2023. The findings of the thematic mapping show that AI and blockchain are emerging techniques and topics such as “smart healthcare”, “patient-centric”, “healthcare management”, “virtual & augmented reality”, “decentralization”, etc. are the potential and new dimensions that can be looked upon in future themes. This study advances knowledge by providing a current and future overview of AI and blockchain integration in the healthcare industry that would create new and enhance existing research streams.

Et al. Satish V. Kakade

Blockchain technology has been incorporated into the Healthcare Internet of Things (IoT) landscape as a revolutionary solution to tackle issues related to the sharing of medical records. This paper presents an innovative method that utilizes Temporal Blockchain for the purpose of Provenance Tracking. The introductory section provides context by delineating the significance of trust and transparency in medical data sharing within the healthcare IoT ecosystem. The study examines current blockchain solutions, delving into frameworks such as Hyperledger Fabric, Ethereum, Corda, and specialized approaches like temporal blockchain. The paper examines the difficulties associated with tracking the origin of data, concerns regarding privacy, problems related to scalability, and the need to comply with regulations. These challenges provide the context for the proposed methodology. The main emphasis is on Temporal Blockchain, integrating temporal elements to improve the tracking of origin and history. The evaluation parameters, such as security, provenance tracking, scalability, interoperability, privacy compliance, and performance, undergo a thorough assessment. The attained values demonstrate a strong emphasis on security at a high level, thorough tracking of origin and history, and strict adherence to privacy regulations. Nevertheless, the need for scalability and interoperability necessitates meticulous consideration. The study showcases the capacity of Temporal Blockchain to establish trust and enhance transparency in the sharing of medical records. The future scope focuses on tackling scalability challenges, improving interoperability, and making continuous optimization efforts. The proposed approach highlights notable accomplishments and emphasizes the continuous development and collaborative aspect of Blockchain-Based Medical Record Sharing in Healthcare IoT.

Lakshay Kumar, Aayush Sharma, Harshal Sharma, Swati Sharma

No abstract is available for this record.

Rocco de Filippis, Abdullah Al Foysal

In an era marked by rapid technological advancement, the fusion of Artificial Intelligence (AI), Machine Learning (ML), and Distributed Ledger Technology (DLT), commonly referred to as blockchain, represents a pioneering frontier in healthcare and psychology.This paper explores the transformative potential of integrating these technologies to reimagine traditional practices and unlock novel approaches to patient care, diagnostics, therapy, and mental health management.Specifically, it investigates the unique and complementary roles that AI, ML, and DLT can play within healthcare and psychology, presenting a detailed roadmap for researchers, practitioners, and stakeholders.Through AI and ML's advanced analytics and predictive capabilities, and blockchain's secure, decentralized data management, this paper demonstrates how these technologies can collectively enhance diagnostic precision, personalize treatment plans, optimize resource allocation, and streamline administrative workflows.Central to this study is a proposed technical architecture, illustrating how AI, ML, and DLT can be integrated within healthcare workflows.This includes using blockchain for secure, verifiable patient data storage and off-chain AI/ML processing for real-time, data-driven insights.Additionally, this paper discusses practical methods, such as zero-knowledge proofs and federated learning, to maintain privacy and regulatory compliance in handling sensitive health data, especially in mental health contexts.Addressing the importance of ethical considerations, this paper highlights best practices in responsible innovation, emphasizing transparency, accountability, and fairness in the deployment of these technologies.Compliance with frameworks like GDPR and HIPAA is discussed as crucial for ensuring patient rights and establishing trust in data handling practices.Moreover, the paper underscores the need for interdisciplinary collaboration, identifying structured models for joint efforts between healthcare professionals, data scientists, and blockchain developers.Examples include cross-disciplinary training sessions, shared project management How to cite this paper:

Dominique Monlezun

Chapter 7 unites the different dimensions explored in each of the earlier chapters into a cohesive whole to understand the artificial intelligence (AI)-powered global public health ecosystem as humanity’s common home: its decentralized organic design (financing and integral sustainable development), framework (data architecture and political economics), inhabitants (culture and demographics), and foundation (ethics and human security balancing national security). It summarizes the key findings for these domains from the earlier chapters while highlighting emblematic AI case uses. It considers financing advances, including in universal health coverage, public–private partnerships, digital global health diplomacy, finance tracking, and value-based health. This chapter moves on to integral sustainable development advances, including in AI for the sustainable development goals, precision agriculture, climate change, affordable clean energy, equity, and generative AI (including ChatGPT). It then considers data architecture advances, including in the United Kingdom’s hybrid data architecture, India’s federated data architecture, swarm learning, gossip learning, blockchain, edge computing, application programming interfaces, augmented public health intelligence, quantum computing, zero-trust security, blockchain, and data solidarity. This chapter then considers political economic advances particularly from the perspective of Political Liberalism–bridging democracies and autocracies, including in data governance models (spanning Europe’s general data protection regulation and Japan’s agile governance), managed strategic competition, and World Health Organization coordination. Finally, this chapter considers AI ethics for the health ecosystem. Particular emphasis is given to how population aging, multicultural diversity, and human security requires more inclusive discussion of diverse perspectives, as through Personalist Social Contract ethics to generate and sustain substantive convergence on the unifying values of human dignity, rights, and sovereignty that then give rise to effective and equitable collective action. This chapter concludes by applying the abovesaid dimensions to concrete AI use cases for the global public health ecosystem that illustrates this integral approach, including ethics by design or embedded AI ethics (within existing ecosystem operations and structures), democratizing AI or personalizing AI (as with end-to-end AI platforms and edge computing expanding and interlinking free and affordable AI services for larger audiences), and ecosystem interoperability (uniting political economic interoperability, data interoperability, and moral interoperability to leverage global resources and insights for local communities leading their own projects).

Pranay Shah, Sushruta Mishra, Angelia Melani Adrian

No abstract is available for this record.

Huwida Said, Nedaa Baker Al Barghuthi, Sulafa Badi, Faiza Hashim · 5 authors

No abstract is available for this record.

K. Bagga, Akhilesh Kasturi, Sonaakshi Shri Kunrarasu, Beena B.M.

No abstract is available for this record.

D. Lakshmi, Isha Kondurkar, Raj Kumar, Roshni Banerjee

No abstract is available for this record.

Wasswa Shafik

New technologies like artificial intelligence (AI) and Blockchain have significantly transformed the telehealth industry. AI can revolutionize healthcare, improving treatment outcomes, enabling personalized medicine, and electronic health record (EHRs) management systems. However, this trend also poses many cybersecurity risks, making telehealth systems an easy target for hackers. Data breaches in telehealth are most often caused by inadequate cybersecurity measures, insider threats, and third-party breaches, which cost economies heavily on the taxpayer's revenue per incident. Telehealth systems' most common types of cyberattacks include ransomware, phishing, and malware attacks. This chapter exhaustively presents various telehealth concerns triggered by cybersecurity from a universal telehealth system perspective, its vulnerability, and what the future could hold for telehealth systems at the facility, patients' level, and other stakeholders. Identifiable attacks and imperatives for telehealth systems, over 15 global significances for medical data, are demonstrated and presented. The effects of these attacks can be identically complex, like data leaks, system downtime, financial losses, and damage to a person's reputation. In order to deal with these risks, the study further demonstrates a privacy and security framework that telehealth organizations can adopt to ensure that privacy and security are attained amidst better quality of services using a multi-pronged cybersecurity strategy that includes regular risk assessments, adequate cybersecurity measures, employee training, and incident response plans. Lastly, highlight how Blockchain can store, secure, and share patient data, including Electronic Health Records (EMR) and medical research data.