Blockchain Papers

Michael J. Diaz, Sai Batchu, Jasmine Tran, Arthur Samia · 5 authors

No abstract is available for this record.

Kelly Richdale

Abstract This chapter explores the transformative role of AI across various dimensions of healthcare, highlighting the interplay between analytical and generative AI in medical imaging, as well as the implications of generative AI in drug discovery and development. It reviews generative AI in the broader framework of P4 medicine—predictive, preventive, personalized, and participatory—and the potential of AI to improve health outcomes, while tackling the rising costs and increased demand for healthcare professionals in aging populations. The requirement for multimodal longitudinal datasets is examined, as well as the challenges around data sparsity and data bias, and the need for data equity. The chapter further evaluates inherent risks and the most relevant aspects of regulation relating to the use of AI in healthcare, personal data protection, and security. In conclusion, the chapter reviews the potential future impact of agentic AI, and technology convergence with robotics, zero-knowledge proofs and blockchain, and quantum computing.

Ravi Teja Potla

The increase in demand of data driven decision making in sensitive fields like healthcare and finance requires machine learning frameworks that maintain strict data privacy and follow regulations. Federated Learning (FL) provides a decentralized way to train models. It allows multiple organizations to learn together from distributed datasets without sharing raw data. But, traditional FL methods, such as Federated Averaging (FedAvg), face issues in real world situations. These issues arise from different data distributions among clients and the risk of information leaks from shared model updates. In this research study, we introduce a new federated learning framework with two main innovations: First the adaptive aggregation strategy that adjusts client contributions based on how stable they are and their quality, and second an optional differential privacy module at the server to make sure privacy guarantees. We tested the framework on two publicly available datasets: a heart disease dataset from the University of California, Irvine (UCI) repository and a large financial dataset from Kaggle. This simulates collaboration between hospitals and financial institutions. Experimental results show that our adaptive aggregation method boosts model accuracy by up to 4.2% compared to FedAvg, while still performing well even with differential privacy applied. The model achieves an AUC of 0.93 and an F1 score of 0.891, with minimal communication overhead. These results confirm the framework’s strength and its ability to support the ethical use of Artificial Intelligence in regulated and data sensitive areas. They also recommend it can scale effectively across larger federated networks.

Aleksandar Stankovic, Marina Marjanović

Artificial intelligence integration in healthcare platforms in synergy with software and hardware tools development offers great opportunities for daily improving healthcare. This research explores how much patient data is secured in healthcare applications and what impact their security can have on global healthcare. Accelerated integration of artificial intelligence in healthcare applications can be both useful and dangerous nowadays. Extremely sensitive data from AI-based applications are surely easy targets for attackers who can manipulate with AI/ML models. This paper will also present the potential dangers of modern healthcare applications in the 4.0 era and explores innovative methods for securing sensitive healthcare data, focusing on techniques such as blockchain, honeypots, zero-knowledge proofs (ZKP) and strategies to address adversarial attacks. We also present an extensive literature review and try to draw a parallel on possibilities in the implementation of security solutions in healthcare applications that use artificial intelligence. Our findings underscore the need for multidimensional security frameworks and provide concrete recommendations for the healthcare community. Ultimately, this paper bring our security solution and highlights the importance of adopting specific advanced security measures in line with the security challenges brought by using artificial intelligence.

Tanisha Bhardwaj, K Sumangali

With the rapid growth of healthcare data and the need for secure, interpretable, and decentralized machine learning systems, Federated Learning (FL) has emerged as a promising solution. However, FL models often face challenges regarding privacy preservation, transparency, and resistance to adversarial attacks. To address these limitations, this paper proposes the Privacy Preserving Federated Blockchain Explainable Artificial Intelligence Optimization (PPFBXAIO) framework, which integrates blockchain technology, Explainable AI (XAI), and optimization techniques to ensure privacy, traceability, and robustness in FL-based systems. PPFBXAIO employs Secure Hash Algorithm 256 (SHA-256) for blockchain-backed secure model updates, Min-Max normalization for feature scaling, and the Levy Grasshopper Optimization Algorithm (LGOA) for optimal feature selection and federated model tuning. The Entropy Deep Belief Network (EDBN) is used as the classifier to enhance classification accuracy and detect attacks. XAI tools like SHAP are utilized to improve model interpretability. Experimental validation was conducted using the Heart Disease dataset from Kaggle and the Wisconsin Breast Cancer dataset. Results showed that PPFBXAIO achieved 95.07% accuracy, 95.44% precision, 96.54% recall, 95.98% F1 score, and reduced training loss by 4.93% for Breast Cancer Wisconsin and achieved 93.07% accuracy, 91.19% precision, 95.39% recall, 93.24% F1 score for Heart Disease dataset. Proposed system has reduced latency by 81 ms, and improved throughput by 109 transactions per second for 100 rounds as compared to traditional models like FedAvg, FL-MPC, FL-RAEC, and PEFL. These results highlight the framework's superior performance, privacy preservation, and practical applicability in decentralized healthcare AI systems.

Alexandra Vultureanu‐Albişi, Costin Bădică, Mirjana Ivanović

The Internet of Things (IoT) paradigm is evolving and the Next-Generation IoT (NG-IoT) ecosystem will incorporate distributed ledger and blockchain technology, AI-adapted components, and intelligent edge solutions that take advantage of edge computing, Artificial Intelligence (AI), networks, and communications. In addition to the low integration of eXplainable Artificial Intelligence (XAI) in the IoT or NG-IoT contexts, the explainability of these systems is rarely evaluated. Due to these limitations, we thoroughly examined the current state of XAI integration with IoT services. We propose a new conceptual framework called eXING-IoT (eXplainability Integrated in the Next Generation IoT) for better NG-IoT systems' explainability integration and evaluation. This includes a list of qualities that future NG-IoT environments should have, thus paving the way for the advancement of NG-IoT beyond the state of the art.

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.

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.

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.

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.

Patrick Silva, Patrick J. Silva, Patrick A. Silva, Patrick A. Silva · 5 authors

Cloud and distributed computing, code repositories, and large language models are democratizing the less computationally intensive use cases of artificial intelligence (AI) in medicine. The convergence and democratization of these powerful tools promises to mobilize and utilize humanity’s knowledge and data, at least the knowledge bases and data that are readily available in the public commons. Healthcare represents a challenge due to fragmentation of the data fabric and governance mechanisms intrinsic to that sector of the economy. Privacy laws, stewardship practices, and the fragmented nature of the patient data journey (medical record silos) create cumbersome impediments to health data sharing, particularly longitudinal patient‐level data. Consequently, obtaining the data necessary to train and operationalize AI in many healthcare and clinical genomics use cases limits the promise of these new technologies in addressing complexities in healthcare. We posit that trust, provenance, and fitness of health data and transaction costs represent challenges that blockchain ledgers and smart digital contracts might address. Here, we present frameworks from some of the great economic thinkers that might help address some of the stewardship and agency issues inherent to health data sharing. Our goal is to promote a more equitable and patient‐centric healthcare data fabric to address current challenges of healthcare.

Cherukupally, Rushil Lingaiah

Background: Large Language Models (LLMs) like ChatGPT-4 Turbo, Claude 4 Sonnet, and DeepSeek-V3 are foundational to modern AI applications. However, a significant gap exists in understanding the direct link between their technical performance and user engagement, their scalability under concurrent load, and the practical performance cost of emerging privacy-preserving technologies. Objectives: This thesis conducts a holistic evaluation of these three leading LLMs to: (1) Compare their performance across latency, accuracy, and client-side resource utilization, and establish the relationship between these metrics and qualitative user engagement scores in various conversational contexts (RQ1). (2) Determine their scalability limits under concurrent user loads and quantify the performance overhead of integrating a zero-knowledge proof privacy protocol (EZKL) (RQ2). Methods: A custom, containerized Python framework was used to systematically test the models. For RQ1, performance and engagement were evaluated in three structured contexts: multi-turn (testing memory), cohesive (testing consistency), and ethical (testing safety) sessions. For RQ2, scalability was measured using Locust to simulate 25 to 200 concurrent users in both a standard centralized setup and a privacy-enhanced EZKL configuration. Key metrics included throughput (RPS), error rates, latency (median and P99), client-side resource consumption, and ZKP generation/verification times. Results: For RQ1, ChatGPT-4 Turbo emerged as the top generalist, showing the best balance of low latency, high accuracy, and strong engagement scores in dynamic multi-turn sessions (e.g., 7.9 personalization score). Claude 4 Sonnet excelled in specialized tasks, achieving a perfect context-switching score (0.0) in cohesive sessions and the highest Harm Avoidance Score (8.0) in ethical sessions, albeit with higher resource usage. DeepSeek-V3 consistently showed the highest latency and resource consumption, negatively impacting its engagement scores. For RQ2, ChatGPT-4 Turbo was the most scalable, peaking at 210 RPS with the lowest error rate. The integration of the EZKL protocol resulted in a catastrophic performance collapse for all models, with throughput dropping to near-zero and latency increasing to hundreds of thousands of milliseconds, rendering it unviable for real-time applications. Conclusions: The study concludes that model selection is highly use-case dependent: ChatGPT-4 Turbo is optimal for scalable, general-purpose applications; Claude 4 Sonnet is superior for high-stakes tasks requiring safety and precision. The findings empirically demonstrate that superior technical performance is a direct enabler of higher user engagement. Finally, current zero-knowledge proof implementations impose a prohibitive performance cost for interactive, scalable AI systems.

Biagio Boi, Christian Esposito

No abstract is available for this record.