Blockchain Papers

Santosh Reddy Addula, Yogesh Ramaswamy, Deepa Dawadi, Zabiha Khan · 6 authors

Blockchain technology is revolutionising healthcare data management with its decentralised ledger, which allows for safe transactions and transparent record-keeping. Better privacy protections are necessary, nevertheless, because patient information is very sensitive. By improving decision-making and facilitating safe, transparent, and efficient handling of medical data, the Mother Optimisation Algorithm (MOA) and blockchain technology are reshaping the healthcare industry. Data security, interoperability, and privacy protection are becoming more important issues due to the exponential growth in volume of sensitive patient data generated by the proliferation of digital healthcare systems and medical equipment based on the Internet of Things (IoT). Secure and transparent data sharing across healthcare providers is made possible using blockchain technology, which offers a decentralised, immutable, and tamper-proof ledger. This helps to avoid data breaches. The MOA also improves healthcare system efficiency by maximising the use of available resources for illness diagnosis and therapy suggestion optimisation. Better patient outcomes, lower operational costs, and more efficient use of medical resources are all results of healthcare predictive analytics that are enhanced by MOA's intelligent, adaptive problem-solving skills. Making decisions in real-time, streamlining medical workflows, and creating personalised treatment plans are all possible with the integration of MOA with blockchain-based healthcare systems. This creates an environment that is trustworthy, intelligent, and patient-centric. Further study is address obstacles such as computational complexity, scalability, and interaction with existing healthcare infrastructures, despite its advantages. To build a stronger, safer, and more efficient healthcare system, future developments should concentrate on optimising MOA's methods, making blockchains more scalable, and integrating AI-driven approaches. This strategy has the ability to revolutionise healthcare by making it safer, more data-driven, and patient-centered. As a result, medical services will be better and more accessible for people all over the world.

K Shruthi, A.S Poornima

Blockchain technology has become an essential tool for enhancing reliability and security across several industries, including the healthcare sector. In this work, we use blockchain technology to establish an append-only chain of transaction blocks that guarantees the confidentiality and integrity of patient health records. Our goals in using blockchain are to safeguard user privacy, give authorized professionals restricted access to medical records, and protect patient data. Doctors are only able to access prescription information with the patient's express consent, providing strong protection for both parties. The blockchain's consensus processes, which need approval from current nodes before new transactions can be added, ensure consistency across blocks. Because they are worried about sensitive data leaks, traditional healthcare systems frequently experience delays in data transmission and enforce stringent access controls. To enhance data sharing and lower the likelihood of data tampering and security breaches, this study will incorporate blockchain technology into healthcare records and data management.

Rohantha Jayasinghe, M.W.K.L Sasanka, Dinithi Athukorala, M.A.D Sandeepani · 6 authors

As digital evidence increasingly growing in significance in healthcare forensics, safeguarding sensitive medical data's confidentiality, integrity, and limited access remains to be an important issue. Existing forensic evidence management systems are subject to data breaches and illegal access since they frequently lack significant privacy-preserving measures. In order to overcome such challenges, this research suggests a Blockchain-Based Custody Evidence Management System for Healthcare Forensics, which combines blockchain technology, machine learning, and encryption methods to improve security, privacy, and accessibility. To ensure accurate and efficient gathering of information, machine learning algorithms are used to extract handwritten and printed text from medical photographs. AES encryption ensures safe storage, while Fully Homomorphic Encryption (FHE) is used for dynamic access level control to protect gathered evidence. Identity verification is made possible via a web-based authentication system that uses Zero-Knowledge Proofs (ZKP) to protect privacy by preventing the disclosure of personal data. By preventing unintended modifications, blockchain technology is used to preserve the custody chain's integrity. Furthermore, machine learning-driven PII detection and masking methods balance the requirement for forensic investigation with privacy compliance by controlling data accessibility according to access entitlements. Based on permitted access levels, the system makes it possible to share safe evidence with law enforcement agencies, such as courts, the police, and other forensic groups. Using blockchain to guarantee data immutability, cryptographic security to restrict access, and artificial intelligence (AI) to safeguard data, this approach enhances the privacy, security, and dependability of handling forensic evidence in medical investigations.

L Prajwal, G. K. Prajwal, R Sagar, Sujay · 6 authors

Blockchain technology has emerged as a transformative force to address major issues in the healthcare sector, such as data privacy, security, and interoperability. Originally introduced as the infrastructure of Bitcoin, the decentralized and tamper-proof nature of the blockchain has subsequently discovered unprecedented promise in applications beyond finance. In healthcare, where sensitive data such as Personal Health Records (PHRs) and Electronic Health Records (EHRs) must be strongly protected, blockchain offers a secure and efficient method of managing and sharing information. This paper examines the application of blockchain to PHR systems to overcome the limitations of traditional cloud-based storage, such as vulnerability to breaches, unauthorized access, and single points of failure. Utilizing the decentralized nature of blockchain architecture, encrypted hashes, and secure key management, the system ensures data integrity, fine-grained access control, and interoperability among healthcare providers without gaps. Furthermore, the utilization of smart contracts streamlines processes such as insurance claims and consent management, reducing administrative burden, and facilitating trust among stakeholders. Regardless of regulatory compliance, scalability, and user adoption issues, research and pilot initiatives continue to advance blockchain integration into healthcare care. Through this paper, our goal is to demonstrate how blockchain can revolutionize the management of personal health records, creating a patientcentric, secure, and transparent healthcare ecosystem that serves patients, providers, and the broader med- ical community.

Sharmila Agnal, Venkata Ramana K., Dinesh Kumar S, Ashwin Ulagappan G

This Project presents a safe and decentralized framework for handling electronic medical records (EMRs) using blockchain technology. The suggested framework guarantees data integrity, confidentiality, and availability through the use of blockchain's distributed ledger system. It allows healthcare providers, patients, and approved staff to safely access and update medical records in real-time while ensuring rigorous privacy controls. This solution overcomes the problems with current EMR systems, including data fragmentation, security risks, and interoperability problems. By leveraging smart contracts and blockchain's cryptographic security, this framework can transform healthcare data management, improve patient care, and enable efficient collaboration among medical professionals.

Ahmad J. Alkhodair

The paper presents a novel decentralized training framework for Ethical Artificial Intelligence (EAI) that leverages blockchain and IPFS technologies. The system addresses significant issues with the reliability, transparency, and ability to handle large amounts of data by including local nodes for data collection and local models generation. And global nodes for data authentication and global models generation. The framework's ability to enhance the development of ethical AI in several fields is emphasized by its design considerations and potential applications, including Healthcare, Finance, Internet of Things (IoT), Cyber Physical Systems (CPS), and Supply Chain Management (SCM).

Ashlesha Hota, Arishmita Biswas, Sanchita Saha, Anup Kumar Barman · 5 authors

No abstract is available for this record.

Salman Muneer, Sagheer Abbas, Asghar Ali Shah, Meshal Alharbi · 8 authors

Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the world. Researchers have applied multiple traditional approaches to making healthcare systems find new solutions for the CVD concern. Artificial Intelligence (AI) and blockchain are emerging approaches that may be integrated into the healthcare sector to help responsible and secure decision-making in dealing with CVD concerns. Secure CVD information is needed while dealing with confidential patient healthcare data, especially with a decentralized blockchain technology (BCT) system that requires strong encryption. However, AI and blockchain-empowered approaches could make people trust the healthcare sector, mainly in diagnosing areas like cardiovascular care. This research proposed an explainable AI (XAI) approach entangled with BCT that enhances healthcare interpretability and responsibility to cardiovascular health medical experts. XAI is significant in addressing cardiovascular prediction issues and offers potential solutions for complex communication and decision-making in cardiovascular care. The proposed approach performs better, with the highest accuracy of 97.12% compared to earlier methods. This achievement shows its ability to tackle complex issues, accessible during healthcare sector communication and decision processes.

Naman Sharma, Manish Bhardwaj

Blockchain and machine learning are transformative technologies with the potential to significantly enhance the healthcare sector by addressing critical challenges such as operational efficiency, data security, and privacy. As a distributed ledger technology, blockchain enables secure and decentralized data sharing among healthcare systems, facilitating the safe exchange of sensitive patient information between hospitals, diagnostic labs, pharmaceutical companies, and patients. Blockchain applications in healthcare can improve patient privacy protection, electronic health record (EHR) management, and counterfeit drug tracking within pharmaceutical supply chains. When integrated with machine learning, blockchain fosters the development of advanced, patient-centric healthcare solutions. Its decentralized architecture ensures data integrity, prevents manipulation, and enhances security, while AI-powered analytics enable personalized medicine, real-time decision-making, and operational cost reduction. This synergy is expected to revolutionize healthcare by enabling applications such as secure medical data storage, pharmaceutical supply chain transparency, remote patient monitoring, and fraud prevention in clinical trials. By leveraging the complementary strengths of these technologies, healthcare systems worldwide can improve patient outcomes, enhance efficiency, and establish a more transparent and resilient infrastructure.

B Madhusudhana Rao, G. V. R. Sai Madhukar, Bankula Nithin Reddy, Sriyan Kumar Voni · 5 authors

This is the Comprehensive AI-Powered Healthcare Management System intended to revolutionize healthcare delivery, overcoming the shortcomings of present systems by integrating multiple sources of information to predict disease onset and administering personalized care, all based on advanced technologies such as Artificial Intelligence and Machine Learning integrated into Blockchain. The system will have early disease detection and tailor-made treatment plans and holistic patient care. The key algorithms include Random Forest, Support Vector Machine, and Neural Networks. It will deploy Convolutional Neural Networks for the analysis of medical images and Natural Language Processing techniques through the application of transformer models such as BERT. Key Technologies to be used are PyTorch, TensorFlow, DialogFlow, and Ethereum. This project shall be developed in phases starting from collecting and integrating diverse health data. Expected output is a fully functional healthcare management platform for the enhancement of patient outcomes, facilitation of greater efficiency by health providers, and secure health data management. These diverse applications have functionalities in improving diagnostic accuracy and patient management in clinics, remote monitoring of patients with chronic diseases, prediction of mental health crisis incidents, and safe storage of patient data through blockchain integration.

T S Namitha, Bipin Kumar, P Pallavi, R Pavana · 5 authors

The existing healthcare claims process suffers from many ills, like fraud, huge paperwork, and delays in claims verification, affecting both patients and insurers badly. This paper represents a blockchain-based health insurance system based on Ethereum smart contracts and IPFS to enhance the security and transparency and ensure automation in claims processing. The proposed system consists of the blockchain layer for automating the policy management, claims processing, and fraud detection via Policy, Claim, and Audit smart contracts; a decentralized storage layer (IPFS) that ensures secure and tamper-proof storage of claim-related documents; and a payment layer that formally settles transactions via smart contracts, thus lessening the company's reliance on intermediaries. Integrating middleware APIs ensures good interoperability among insurers, hospitals, patients, and administrators-real-time tracking of claims and compliance with regulatory demands. This will be a global, scalable, fraud-resistant model that optimizes claims settlement and minimizes administrative overhead and maximizes user satisfaction. This paper underscores how health insurance can benefit from blockchain technology.

Bidyutmala Saha

The medicine Supply Chain System (SCS) is an efficient functional network around the essential enterprise. A robust medicine SCS has become essential in the post-COVID era to ensure timely access to life-saving medications, facilitate efficient distribution of vaccines, prevent shortages, and ensure resilience during health crises. Corruption, fraud, and manipulation are all potential threats to a supply chain that is managed centrally. The pharmaceutical supply chain can be more secure and transparent by utilizing the different inbuilt cryptographic algorithms, smart contracts, and consensus mechanisms. This technology also provides a secure way of data sharing, and autonomous management as a distributed ledger. The integration of blockchain technology into supply chain management, or SCM, is examined in this study in order to develop a system architecture that is trustworthy, transparent, authentic, and safe. In addition to specific algorithms outlining the operation of our proposed solution, we also provide system architecture. We also concentrate on applying blockchain technology to solve the problem of hierarchical transactions.

Gowthami Jaganathan, Shanthi Natesan

In the modern era of digitalization, integration with blockchain and machine learning (ML) technologies is most important for improving applications in healthcare management and secure prediction analysis of health data. This research aims to develop a novel methodology for securely storing patient medical data and analyzing it for PCOS prediction. The main goals are to leverage Hyperledger Fabric for immutable, private data and to integrate Explainable Artificial Intelligence (XAI) techniques to enhance transparency in decision-making. The innovation of this study is the unique integration of blockchain technology with ML and XAI, solving critical issues of data security and model interpretability in healthcare. With the Caliper tool, the Hyperledger Fabric blockchain's performance is evaluated and enhanced. The suggested Explainable AI-based blockchain system for Polycystic Ovary Syndrome detection (EAIBS-PCOS) system demonstrates outstanding performance and records 98% accuracy, 100% precision, 98.04% recall, and a resultant F1-score of 99.01%. Such quantitative measures ensure the success of the proposed methodology in delivering dependable and intelligible predictions for PCOS diagnosis, therefore making a great addition to the literature while serving as a solid solution for healthcare applications in the near future.

Maida Maqsood, Hafsa Hamid Butt, Muhammad Awais Ali, Caleb Chidozie Chinedu · 5 authors

The purpose of this chapter is to examine the ethical concerns and benefits associated with the integration of Artificial Intelligence (AI), particularly chatbots, in education. A comprehensive literature review was conducted to identify and analyze ethical considerations related to AI in education. The applicability of the web3 application in the identification and analysis of ethical concerns of AI chatbots is also explored. The findings revealed a spectrum of ethical concerns spanning fairness, transparency, privacy, autonomy, and educational inequality. Ethical concerns also include the misuse of private data, algorithmic biases, surveillance, and threats to job security. Conversely, the benefits of AI in education encompass improved learner experiences, enhanced teaching efficiency, and personalized learning opportunities. Chatbots, in particular, demonstrate potential in fostering engagement, increasing interest in subjects, and offering immediate support to students.

Yi Xie, Xiaoliang Chen, Huiwen Yang, Honglin Wang · 9 authors

No abstract is available for this record.

Gabriel Chukwunonso Amaizu, Akshita Maradapu Vera Venkata Sai, Sanjay Bhardwaj, Dong‐Seong Kim · 6 authors

The increasing prevalence of cancer necessitates advanced methodologies for early detection and diagnosis. Early intervention is crucial for improving patient outcomes and reducing the overall burden on healthcare systems. Traditional centralized methods of medical image analysis pose significant risks to patient privacy and data security, as they require the aggregation of sensitive information in a single location. Furthermore, these methods often suffer from limitations related to data diversity and scalability, hindering the development of universally robust diagnostic models. Recent advancements in machine learning, particularly deep learning, have shown promise in enhancing medical image analysis. However, the need to access large and diverse datasets for training these models introduces challenges in maintaining patient confidentiality and adhering to strict data protection regulations. This paper introduces FedViTBloc, a secure and privacy-enhanced framework for medical image analysis utilizing Federated Learning (FL) combined with Vision Transformers (ViT) and blockchain technology. The proposed system ensures patient data privacy and security through fully homomorphic encryption and differential privacy techniques. By employing a decentralized FL approach, multiple medical institutions can collaboratively train a robust deep-learning model without sharing raw data. Blockchain integration further enhances the security and trustworthiness of the FL process by managing client registration and ensuring secure onboarding of participants. Experimental results demonstrate the effectiveness of FedViTBloc in medical image analysis while maintaining stringent privacy standards, achieving 67% accuracy and reducing loss below 2 across 10 clients, ensuring scalability and robustness.

Anna Maria Fink, Johanna Nattenmüller, Stephan Rau, Alexander Rau · 10 authors

OBJECTIVES: This study evaluated the effect of enhancing a GPT-4 model with retrieval-augmented generation on its ability to diagnose and classify traumatic injuries based on radiology reports. MATERIALS AND METHODS: In this prospective proof-of-concept study, we used retrieval-augmented generation as a zero-shot learning approach to provide expert knowledge from the RadioGraphics top ten reading list for trauma radiology to the GPT-4 model, creating the context-aware TraumaCB. Radiological report findings of 50 traumatic injuries were independently generated by two radiologists. The performance of the TraumaCB compared to the generic GPT-4 was evaluated by three board-certified radiologists, assessing the accuracy and trustworthiness of the chatbot responses in the 100 reports created. RESULTS: The TraumaCB achieved 100% correct diagnoses, 96% correct classification, and 87% correct grading, outperforming the generic GPT-4 with 93% correct diagnoses, 70% correct classification, and 48% correct grading. TraumaCB sources consistently achieved a median rating of 5.0 for explanation and trust. Challenges encountered mainly involved traumatic injuries lacking widely accepted classification systems. CONCLUSION: Augmenting a commercial GPT-4 model with retrieval-augmented generation improves its diagnostic and classification capabilities, positioning it as a valuable tool for efficiently assessing traumatic injuries across various anatomical regions in trauma radiology. KEY POINTS: Question Retrieval-augmented generation has the potential to enhance generic chatbots with task-specific knowledge of emergency radiology. Findings The TraumaCB excelled in accuracy, particularly in injury classification and grading, and provided explanations along with the sources used, increasing transparency and facilitating verification. Clinical relevance The TraumaCB provides accurate, fast, and transparent access to trauma radiology classifications, potentially increasing the efficiency of image interpretation in emergency departments and enabling customized reports based on local or individual preferences.

Hongtao Zhu, Aoli Wu, Siyu Zhang, Hexin Cui · 5 authors

In our pursuit of sustainable innovation for Intangible Cultural Heritage (ICH), we have adopted a methodology driven by the co-creation of human and artificial intelligence (AI). Our inclusive community, consisting of diverse stakeholders such as folk inheritors, professional choreographers, cultural center staff, square dance enthusiasts, designers, and AI engineers, forms a robust foundation for innovative practices. The community’s outputs, including dataset, experimental dance, creative short film, digital 3D works, Non-Fungible Tokens (NFTs), and an App, are multi-modal transformable. We underscore the communal utilization of resources across diverse practices and advocate for the transformation of outputs across various modalities. Importantly, each of these practices integrates AI technology into the workflow, positioning it as a pivotal enabler for fostering sustainable innovation within the domain of ICH.

Nikhil Sethi, Charul Rathore, Dilpreet Singh

The present study focuses on the possible use of the emerging technology of blockchain in ensuring data management security in clinical trials. With the determination of the chief researchers and clinical investigations becoming more and more complex and international, achieving data quality and integrity, transparency, and legal compliance becomes imperative. By offering a distributed and immutable time-stamped ledger, issues of data revisions, selective data release, and the usually time-consuming issue of compliance auditing are well addressed. With this technology, it is possible to conduct surveillance of multi-center studies without compromising the confidentiality of patients while allowing the researchers to have unbiased information. When it comes to internal accountability, the use of the blockchain will create a situation whereby no alteration of the documents will take place. Thus, regulatory oversight is improved with the engagement of these parties. In addition, it makes sure that the need for bias in the reporting of outcomes is avoided in all trials and all results reported whether positive or negative. In order to address clinical trial data management and clinical trial outcomes' validity and reliability, this review provides reputation management through digital ledger technology in the real world.

Víctor Leiva, Cecília Castro

This article examines the transformative potential of blockchain technology and its integration with artificial intelligence (AI) in clinical trials, focusing on their combined ability to enhance integrity, operational efficiency, and transparency in the data governance. Through an in-depth analysis of recent advancements, the article highlights how blockchain and AI address critical challenges, including patient data privacy, regulatory compliance, and security. The article also identifies key barriers to adoption in the mentioned integration, such as scalability limitations, association with existing healthcare systems, and high implementation costs. By presenting a comprehensive overview of the current research and proposing strategic directions, this work emphasizes how the synergy between blockchain and AI can revolutionize clinical trials through process automation, improved stakeholder trust, and robust transparency.

Roger T. Tomihama, M. C. Wilkinson, Sharon C. Kiang

Blockchain technology (BCT) enables the building of a distributed decentralized network that securely stores and exchanges unchangeable data, controlled by individual users. In health care, BCT may help streamline interoperability and information transmission while guaranteeing medical record authenticity and safeguarding patient privacy. Possible applications in radiology include patient-controlled image sharing, facilitation of multiinstitutional research, and artificial intelligence integration. Radiologists should stay informed of BCT given its ongoing improvements and unique potential to support the specialty's needs.

Ming Li, Arjun Rachana Harish, Chenglin Yu, Ying Yu · 6 authors

To promote the sharing of medical data assets (MDAs) in a more secure and sustainable manner, this article presents a blockchain-based MDA sharing framework. The contributions of this article are threefold. First, we designed a layered-architecture to decouple the privacy-preserving responsibilities among technologies considering the incentive rewarding and parallelization of execution. Second, we introduce zero-knowledge proofs (ZKPs) in smart contracts with a group signature to construct a supervisory privacy-preserving sharing mechanism, which can be executed in a decentralized environment to protect the privacy of MDAs. Third, we introduce an incentive mechanism that motivates MDA sharing by capturing the decentralized features of the participants to deliver fair rewards. The experiments show that our framework achieves a comprehensive privacy protection on sharing MDAs, comparing with single blockchain sharing schema, with only 2.2% sacrifice on TPS (throughput/second). Moreover, our framework has better potential for large-scale application due to the paralleled execution on ZKP-based smart contracts.

Tehseen Mazhar, Sunawar Khan, Tariq Shahzad, Muhammad Amir Khan · 7 authors

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized.

Khadija Tlemçani, Kebira Azbeg, El Mehdi Saoudi, Leila Fetjah · 6 authors

In the evolving landscape of health information management, the application of blockchain and edge computing technologies to chronic disease management remains underexplored, despite the urgent need for scalable, secure, and real-time solutions. This systematic review examines the integration of these technologies in healthcare, with a specific focus on diabetes management. We analyzed 52 studies, categorizing findings into three key areas: enhanced data security and privacy through decentralized frameworks and tamper-proof storage; real-time data processing, enabled by edge computing for immediate analytics and alerts; and scalability, achieved via hybrid architectures that optimize data handling. Our review identifies critical gaps in the existing literature, particularly the lack of tailored approaches for chronic disease contexts like diabetes, and outlines future directions for developing robust, secure, scalable, and real-time data solutions. This study provides a foundation for innovation in healthcare technology that can significantly impact diabetes care and chronic disease management.