Blockchain Papers

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.

Xiao-Yang Liu, Rongyi Zhu, Daochen Zha, Jiechao Gao · 7 authors

The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science. However, concerns regarding data privacy have emerged, especially when multiple stakeholders aim to collaboratively enhance LLMs using sensitive data. In this scenario, federated learning becomes a natural choice, allowing decentralized fine-tuning without exposing raw data to central servers. Motivated by this, we investigate how data privacy can be ensured in LLM fine-tuning through practical federated learning approaches, enabling secure contributions from multiple parties to enhance LLMs. Yet, challenges arise: (1) despite avoiding raw data exposure, there is a risk of inferring sensitive information from model outputs, and (2) federated learning for LLMs incurs notable communication overhead. To address these challenges, this article introduces DP-LoRA, a novel federated learning algorithm tailored for LLMs. DP-LoRA preserves data privacy by employing a Gaussian mechanism that adds noise in weight updates, maintaining individual data privacy while facilitating collaborative model training. Moreover, DP-LoRA optimizes communication efficiency via low-rank adaptation, minimizing the transmission of updated weights during distributed training. The experimental results across medical, financial, and general datasets using various LLMs demonstrate that DP-LoRA effectively ensures strict privacy constraints while minimizing communication overhead.

Harsh Bansal, Divya Gupta, Darpan Anand

Healthcare industry is constantly evolving because of rapid technological breakthroughs and changing patient needs. Use of innovations in healthcare using modern technologies is revolutionizing the delivery of health care, enhancing patient outcomes, and boosting operational effectiveness, data security, and privacy. Telemedicine and remote patient monitoring have become prominent, especially after the COVID-19 pandemic. Issues with traditional telemedicine and remote patient monitoring, which rely majorly on technology such as privacy and security concerns, interoperability issues, decision making based on large amounts of health data generated can be resolved using technologies such as blockchain and artificial intelligence wherein blockchain ensures decentralization, immutability of data facilitating easy data sharing, and consent administration and artificial intelligence enables clinical decision support decision and personalized healthcare with a focus on preventive instead of reactive health care ( Gupta, S., et al., 2023 ). This chapter reviews the use of blockchain and AI in some significant telemedicine and remote patient monitoring projects and how they can improve the efficiency and functionality of current healthcare. A patient-centric framework has been proposed using components of blockchain and artificial intelligence such as decentralization, distributed ledger technology, consensus mechanisms, smart contracts, immutability, machine learning, deep learning in telemedicine, and remote patient monitoring. Major challenges such as legal and regulatory considerations, ethical concerns, integration and implementation, adoption, and acceptance have been identified and addressed. Overall, the use of blockchain and artificial intelligence in telemedicine and remote patient monitoring has a huge potential to transform the way healthcare is provided.

CM Naga Sudha, J. Jesu Vedha Nayahi

Medical and health related information about patients qualifies as sensitive and personal information. There are many laws such as HIPAA that prohibit sharing of a patient’s intimate medical data to third party organizations without the full consent of the patient. With this in consideration, when the actual medical health record systems are analysed, vulnerabilities that threatens the confidentiality and integrity of the data were found. The other consideration about the current system is the fact that patients do not have control over their data. Therefore, a decentralized ledger can resolve such security issues, where can have control on their own data. Such a decentralization can be implemented by utilizing blockchain technology. Also, within a decentralized system, many number of participants would be connected as network participants. Hence, a permissioned blockchain system using Ethereum helps in facing these attacks. The proposed Ethereum based Electronic Health Record storage system uses InterPlanetary File System (IPFS), for storing the patient’s sensitive data that can be encrypted and only after duly authorized by the patient, a person can view or change it. Further, the proposed Ethereum based Electronic Health Record storage system system can be used to protect other kinds of sensitive data without being limited to health data.

Poornima G. Naik, Vidya Laxmikant Badadare, Rajani S. Kamath

In today's digital age, the healthcare sector is experiencing a significant transformation due to the pervasive influence of Information Technology (IT). There is an abundance of sources for generating and accessing patients' health data, and safeguarding the management of this data has become of utmost importance. Blockchain technology, at the forefront of technological innovation, offers a promising solution for secure data storage within the healthcare domain. In the context of current research the authors, have developed a sophisticated smart contract designed specifically for the storage and management of patients' medical records. This smart contract incorporates a range of functionalities, including the creation of new records, authorization for newly generated records, and the ability to retrieve stored records. Uploading the test reports on IPFS server has been deferred for consideration in future scope. The smart contract is meticulously designed and rigorously tested on the Ethereum Blockchain framework. The research objective is to elucidate the pivotal role that blockchain technology plays in ensuring the security and integrity of healthcare recordkeeping, utilizing the robust Ethereum framework as authors’ platform of choice. The research aims to shed light on the transformative potential of blockchain in enhancing the trustworthiness and privacy of patient data within the healthcare sector.

Shilpa Vinchurkar, Shailesh Kediya, Tushar Somnathe, Yogita Sure · 6 authors

Blockchain technology is a constant area for more evolution and development. It is a network of blocks that protects information and upholds interpersonal trust no matter how far apart people are Decentralization, and transparency are important characteristics that could help in addressing critical difficulties in healthcare, like incomplete records at the point of care and restricted access to patients' own health information. There are several difficulties and problems with data management, security, openness, and user and record privacy even with smart healthcare systems (SHS). This study examines the health care system's application areas where blockchain technology can be used for SHS's efficient functioning. It does so by examining the most recent expert opinions and resent researches done. It discusses the research directions and use cases of blockchain in healthcare domain to produce an in-depth overview of the data management and storage technology in health care system.

Elizabeth A Tissier, A. Berglund, Gabrielle J Johnson, Zakary A Sanzone · 8 authors

Background and objective Physician credentialing and verification in the medical education setting are challenging for the modern workforce. The credentials verification process may be time-consuming and challenging for participants. Blockchain technology is a potential resource for authenticating records with reduced administrative burden and time spent. This study investigates whether the use of blockchain technology reduces the time until verification of a participant's credentials. Methods An anonymous letter designation was assigned to 23 medical students. All students enrolled in, and completed, a course designed and run by the Edward Via College of Osteopathic Medicine at Auburn (VCOM) as part of the routine medical education curriculum. At the completion of the training, a credentials certificate was produced, which showed course completion. The anonymous letter designation was utilized in the creation of the certificates. The letter designations were shared with an anonymous investigator. No student names were shared with the investigator. The investigator posed as an employing/credentialing entity and contacted VCOM to record the time required to verify the credentials certificate indicating course completion. The elapsed time until credentials verification was completed for each student in the current system (CS) was recorded. Subsequently, the credentials certificate was minted as a blockchain-based, non-fungible token (NFT) and uploaded to a document software management system. An investigator again posed as an employing/credentialing entity and utilized this system to verify the credentials of the 23 students in the study using the NFT system. The times elapsed until verification of credentials were recorded as the NFT pathway. Data from the NFT pathway and non-NFT pathway were compiled and reviewed. Results Data were normally distributed per the Andersen-Darling Test. A t-test (Welch's method) was performed. The mean time of 111,214 seconds (30.89 hours or 1.29 days) in the CS varied significantly from the mean time of 14 seconds in the NFT blockchain system (p<0.01). The standard deviation of 56,568 seconds in CS varied significantly from 9.9178 seconds in the NFT blockchain (p<0.01). Conclusions The NFT/blockchain system reduces the mean time until the credential verification is completed and reduces the variance seen in time until credentialing is completed. The NFT/blockchain system may significantly bring down the administrative burden and time spent in the credentialing process.

G. Senthilkumar, Aravindan Srinivasan, J. Venkatesh, Ramu Kuchipudi · 6 authors

Even if big cities are working globally to build the infrastructure for smart cities and there is a greater emphasis on the security of electronic health records, patient privacy is frequently compromised. Previous attempts to combat this have left patients with largely unavailable data. Currently used record-management systems struggle to strike a balance between data privacy and patient and provider access. Blockchain, a new technology, the ability to share data in a decentralized and transactional manner. To balance the accessibility and privacy of electronic health records, blockchain technology may be applied in the healthcare industry. The blockchain-based architecture we present in this work enables patients, healthcare professionals, and third parties to access medical information in a secure, efficient, and straightforward manner while maintaining the privacy of sensitive patient data. Modern cryptographic techniques are used in our architecture, Ancile, to increase security. The usage of smart contracts developed on the Ethereum blockchain improves access control and data obscuration. This article will look at how Ancile works with the varied needs of patients, providers, and third parties in order to understand how the framework might address reoccurring privacy and security concerns in the healthcare industry.

Authors unavailable

The International Conference of Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics is an annual gathering of experts, researchers, and professionals from around the world who share a passion for advancing the fields of artificial intelligence, blockchain, cloud computing, and data analytics.The conference provides a platform for knowledge exchange, networking, and collaboration in these rapidly evolving domains.Our conference is dedicated to exploring the latest research, trends, and best practices in artificial intelligence, blockchain, cloud computing, and data analytics.We seek to create an atmosphere of learning, sharing, and innovation where experts can come together to exchange ideas and collaborate on new projects.At our conference, attendees can expect to hear from a variety of thought leaders, industry professionals, and academics who are at the forefront of their fields.We offer keynote speeches, panel discussions, and technical sessions covering a wide range of topics, from machine learning and natural language processing to distributed ledgers and decentralized applications.

Mohamed Yousuff, J. Jayashree, J. Vijayashree, R. Anusha

In contemporary times, technology is exerting a transformative influence on every facet of human existence. Presently, it is intricately intertwined with nearly all aspects of modern society. Various technological advancements have surfaced recently, including the emergence of blockchain. In order to effectively monitor organizational assets, the utilization of blockchain technology, a decentralized and unalterable ledger, could be employed. Utilizing blockchain technology facilitates secure and cost-effective monitoring and exchange of a wide range of tangible or intangible assets. The blockchain network can monitor and record various activities, such as requests, purchases, accounting, operations, and other related functions. Furthermore, because all individuals within the network have access to identical and current information, it is possible to oversee each transaction stage, thereby increasing confidence and facilitating the emergence of novel prospects and efficacy. The fundamental components of a blockchain include distributed ledgers, immutable data, and intelligent contracts. The blockchain process involves recording individual transactions as discrete blocks of data, which are subsequently linked to the preceding blocks to enable the system’s operation. The emergence of advanced technologies, such as blockchain, has provided a viable solution for addressing the monitoring of patients affected by the coronavirus. The utilization of blockchain technology has the potential to combat pandemics through its ability to facilitate the timely identification of epidemics, safeguarding of sensitive personal information through smart contracts, and streamlined exchange of information. This chapter will explore various use cases of blockchain technology, highlighting the implementation of novel tracking systems enabled by this technology.

Sophiya Rumovskaya, Andrey Litvin

Blockchain and artificial intelligence (AI) are two of the most disruptive modern technologies. A blockchain is a distributed ledger capable of storing data in blocks. The data stored on a blockchain is immutable and secured with cryptography. The blockchain can guarantee data security without the involvement of a third party. The integration of it with AI to create distributed artificial intelligence (DAI) is increasingly being used in various areas of human activity. AI can increase the efficiency of blockchains by streamlining computations and processes, reducing the burden on miners and decreasing latency. The latter results in faster transactions and reduced carbon footprint of the blockchain technology. Blockchain can help develop explainable AI by means of accessing an immutable record of unstructured medical data and processes used by the system in its decision-making process. The purpose of this narrative review is to analyze the possibilities of using a decentralized AI in medicine.

Nazeer Shaik, Nihar Ranjan Kar, Blessy Thankachan, Amit Kumar Pathak · 6 authors

To address the complex issues of detecting fraudulent practices in healthcare insurance, this research employs sophisticated machine learning, especially the Long Short-Term Memory (LSTM) model, to provide a complete framework for reliable fraud detection. The study meticulously examines performance metrics such as accuracy, precision, recall, and the F1 score by employing and evaluating the LSTM model across two distinct datasets-Dataset A (198810 samples) and Dataset B (434319 samples)-illuminating the model's capacity to detect fraudulent activities while minimizing misclassifications. The evaluation process, as shown by confusion matrices displayed as percentages, reveals the model's strengths and points up areas for improvement. This study makes an important contribution to the field of fraud detection by providing practical insights to strengthen healthcare insurance against misleading practices. This research proclaimers a paradigm shift by combining innovative methodologies, extensive dataset curation, and stringent evaluation, ushering in increased security, transparency, and efficacy in healthcare insurance fraud detection, ultimately fostering a future of resilient and precise fraud detection mechanisms.

Ghazi Farouk, Tareck Alsamara

This article presents an in-depth study of the legal landscape surrounding blockchain technology in the healthcare sector, with a special focus on case studies from European countries. Analyzing the existing legal framework and regulations, the research highlights the challenges and opportunities associated with the adoption of blockchain in healthcare. The most important research areas are data protection, security, consent, liability, and compliance. Through a comparative analysis of various European countries, the article illuminates the differences in legal approaches and points out possible areas of harmonization. The results clarify the legal aspects that must be addressed to ensure the integration of blockchain technology into healthcare systems, innovation while protecting patients' rights, and compliance with regulatory requirements.

Naresh Kshetri, James Hutson, G. Revathy

Blockchain as a digital ledger for keeping records of digital transactions and other information, it is secure and decentralized technology. The globally growing number of digital population every day possesses a significant threat to online data including the medical and patients’ data. After bitcoin, blockchain technology has emerged into a general-purpose technology with applications in medical industries and healthcare. Blockchain can promote highly configurable openness while retaining the highest security standards for critical data of medical patients. Referred to as distributed record keeping for healthcare systems which makes digital assets unalterable and transparent via a cryptographic hash and decentralized network. The study delves into the security and safety improvement associated with implementing blockchain in AI-based healthcare systems. Blockchain-enabled AI tackles the existing issues related to security, performance efficiencies, and safety in healthcare systems. This study also examined the implementation of Artificial Intelligence (AI) in healthcare and medical industry, potential areas, open questions concerning the blockchain in healthcare systems. Finally, the article proposed an AI-based healthcare blockchain model (healthAIChain) to improve patients’ data and security.

Uttam Ghosh, Debashis Das, Pushpita Chatterjee, Nadine Shillingford

No abstract is available for this record.

Nima S. Ghorashi, Murwarit Rahimi, Reza Sirous, Ramin Javan

INTRODUCTION: Although blockchain technology and smart contracts are garnering attention in various sectors, their applications and familiarity within the realm of radiology remain largely unexplored. Blockchain, a decentralized digital ledger technology, offers secure, transparent, and resilient data management by distributing the verification process across a network of independent entities. This decentralized technology presents a possible solution for a range of healthcare challenges, from secure data transfer to automated verification processes. To address such challenges in the context of medical imaging, blockchain could provide different approaches, including smart contracts, machine learning algorithms, and the secure dissemination of large files among key stakeholders such as patients, healthcare providers, and institutions. This manuscript aims to explore the current attitudes and perspectives of trainees and radiologists to the utilization of blockchain technology and smart contracts in clinical radiology. Additionally, the study provides an in-depth analysis of the potential applications for incorporating blockchain into radiology. METHODS: After obtaining The George Washington University Committee on Human Research Institutional Review Board (IRB) approval, we conducted a 10-question survey among radiologists and trainees at several institutions and private practices. Surveys were created via the Google Forms application and were emailed to potential participants. Participants were asked about their current academic level (medical student, resident/fellow, academic radiologist, private practice radiologist, others), their knowledge level about the field of imaging informatics and blockchain and smart contract technologies, their level of interest in learning more about blockchain and smart contracts, and their opinion about possible applications of blockchain and smart contract in the future of medical imaging. RESULTS: A total of 118 survey requests were distributed; 83 were returned, reflecting a 70.3% overall response rate. Of these, 19 were sent to private practices with a 15.8% response rate (3/19), and 99 to academic centers, yielding an 80.8% response rate (80/99). The survey respondents demonstrated a strong interest and need to further understand these technologies among radiologists and trainees. This study focuses on key components of this technology as it relates to healthcare and the practice of radiology, including data storage, patient care, secure communication, and automation, as well as strengths, weaknesses, opportunities, and threats (SWOT) analysis. DISCUSSION: To our knowledge, this is the first study to investigate and establish a baseline for the current perspectives on the application of blockchain technology and smart contracts in clinical radiology amongst trainees and radiologists across academic and private settings. Incorporating blockchain and smart contracts technologies into the field of radiology has the potential to achieve greater efficiency, security, and patient empowerment. However, the adoption of this technology comes with challenges, such as infrastructure, interoperability, scalability, and regulatory compliance. Collaboration between radiologists, hospital administration, policymakers, technology developers, and patient advocacy organizations will help guide and advance our understanding of the potential applications of blockchain and smart contracts in radiology and healthcare.

R. Sasikumar, P. Karthikeyan

Electronic Health Records (EHRs) play a vital role in the healthcare domain for the patient survival system. They can include detailed information such as medical histories, medications, allergies, immunizations, vital signs, and more. It can help to reduce medical errors, improve patient safety, and increase efficiency in healthcare delivery. EHR approaches are proven to be an efficient and successful way of sharing patients’ personal health information. These kinds of highly sensitive information are vulnerable to privacy and security associated threats. As a result, new solutions must develop to meet the privacy and security concerns in health information systems. Blockchain technology has the potential to revolutionize the way electronic health records (EHRs) are stored, accessed, and utilized by healthcare providers. By utilizing a distributed ledger, blockchain technology can help ensure that data is immutable and secure from tampering. In this article, a Hyperledger consortium network has been developed for sharing health records with enhanced privacy and security. The attribute based access control (ABAC) mechanism is used for controlling access to electronic health records. The use of ABAC on the network provides EHRs with an extra layer of security and control, ensuring that only authorized users have access to sensitive data. By using attributes such as user identity, role, and health condition, it is possible to precisely control access to records on blockchain. Besides, a Gaussian naïve Bayes algorithm has been integrated with this consortium network for prediction of cardiovascular disease. The prediction of cardiovascular is difficult due to its correlated risk factors. This system is beneficial for both patients and physicians as it allows physicians to quickly identify high-risk patients and easily provide them with patient severity level using feature weight prediction algorithms. Dynamic emergency access control privileges are used for the emergency team and will be withdrawn once the emergency has been resolved, depending on the severity score. The system is implemented with the following medical datasets: the heart disease dataset, the Pima Indian diabetes dataset, the stroke prediction dataset, and the body fat prediction dataset. The above datasets are obtained from the Kaggle repository. This system evaluates system performance by simulating various operations using the Hyperledger Caliper benchmarking tool. The performance metrics such as latency, transaction rate, resource utilization, etc . are measured and compared with the benchmark.

Ritik Kashwani, Hemant Sawhney

Blockchain technology and the metaverse have the potential to revolutionize dentistry and healthcare by enhancing data security, patient empowerment, disaster victim identification, and the delivery of dental services. In this review, we discuss the current state of the art in the field of dentistry and the future of dentistry, highlighting the advantages and challenges of utilizing blockchain technology for disaster victims’ identification. Blockchain's applications in Disaster Victim Identification (DVI) offer a humanitarian dimension, helping bring solace to families in times of tragedy. Moreover, blockchain's potential to establish virtual health clinics and telemedicine platforms could bridge healthcare gaps in underserved regions.

Gifar Arif Haryadi, Allwinnaldo, Jae‐Min Lee, Dong‐Seong Kim

In the domain of healthcare management, conventional paper-based prescription systems manifest vulnerabilities. Addressing this, emerging solutions leverage blockchain and Non-Fungible Tokens (NFTs) to augment e-prescription processes. However, existing research needs comprehensive simulation insights into these NFT-based systems. This paper presents a pragmatic NFT-Integrated E-Prescription Management Smart Contract model to bridge this gap. The model capitalizes on blockchain’s security and NFTs’ attributes to enhance prescription traceability, ownership, and security. It streamlines prescription workflows, facilitating seamless interaction between healthcare providers and pharmacies while also introducing precise ownership control through NFTs. Implemented with a Role-Based Access Control system, authorization is exclusively granted to authorized entities, thereby bolstering security. A comparative analysis reveals distinct disparities in ownership management and prescription expiration. Furthermore, an assessment of cost-effectiveness and robust security measures, encompassing NFT integration, is conducted to safeguard sensitive healthcare data. The model’s applicability is demonstrated through public and local test network deployment. This paper addresses the existing research gap by furnishing comprehensive simulation insights, advancing the comprehension of NFT-based prescription systems.

Abdullah Lakhan, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Mohd Khanapi Abd Ghani · 8 authors

No abstract is available for this record.

Deepansha Chhabra, Meng Kang, Victoria L. Lemieux

This chapter contributes to research on the issue of data double spending, or unauthorized secondary use of individuals&s; data. The chapter describes two solutions that provide decentralized marketplaces for individuals to share their health data for purposes of AI-driven health research – one that uses the Hyperledger Indy/Aries protocol – the ‘Self-Sovereign’ data marketplace – and the other an Ethereum-based solution – the ‘Ocean Protocol’ data marketplace. Based on an implementation of applications using both protocols, the chapter evaluates the strengths and weaknesses of each data marketplace vis-a-vis adherence to fair data processing principles and protecting individuals from data double spending. The chapter contributes to a clearer articulation of fair blockchain-based data processing and the issue of data double spending, an assessment of how well each solution addresses the issue, and possible directions for research aimed at preventing data double spending.