Blockchain Papers

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.

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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.

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.

Christopher P. Albertyn, Svitlana Surodina, Bo Tan, Tina Woods · 7 authors

UNSTRUCTURED Global healthcare systems need to evolve to ensure optimal, safe, and ethical utilisation of health data and the latest digital technologies, such as Artificial Intelligence (AI), Privacy Enhancing Technologies (PETs), and Distributed Ledger Technologies (DLTs), to meet the challenges of a global ageing population. Simultaneously, the increasing capabilities of remote measurement technologies and the proliferation of 5G networks demonstrates that digital technologies are now more accessible to a much larger population, offering an opportunity for decentralised democratised health data use that supports individual agency. Given the significant international human and economic cost of cognitive decline and dementia, we propose that a person-centred decentralised health data ecosystem, underpinned by these emerging technologies and opportunities, would reduce burden on cognitive healthcare systems by intervening earlier, accelerate clinical research innovation in dementia, and extend cognitive healthspan. Crucially, we argue for the importance of including the individual, as well as other key stakeholders, in the development, continuing operation, and as a shared beneficiary of any potential accrued value emerging from this ecosystem.

Xueping Liang, Juan Zhao, Yan Chen, Eranga Bandara · 5 authors

BACKGROUND: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site. OBJECTIVE: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs. METHODS: We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain. RESULTS: We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra-based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. CONCLUSIONS: Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain.

Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne

No abstract is available for this record.

Rabimba Karanjai, Edward Li, Lei Xu, Weidong Shi

The introduction of large language models (LLMs) like ChatGPT and Google Palm2 for smart contract generation seems to be the first well-established instance of an AI pair programmer. LLMs have access to a large number of open-source smart contracts, enabling them to utilize more extensive code in Solidity than other code generation tools. Although the initial and informal assessments of LLMs for smart contract generation are promising, a systematic evaluation is needed to explore the limits and benefits of these models. The main objective of this study is to assess the quality of generated code provided by LLMs for smart contracts. We also aim to evaluate the impact of the quality and variety of input parameters fed to LLMs. To achieve this aim, we created an experimental setup for evaluating the generated code in terms of validity, correctness, and efficiency. Our study finds crucial evidence of security bugs getting introduced in the generated smart contracts as well as the overall quality and correctness of the code getting impacted. However, we also identified the areas where it can be improved. The paper also proposes several potential research directions to improve the process, quality and safety of generated smart contract codes.

Badal Gami, Manav Agrawal, Deepak Mishra, Danish Quasim · 5 authors

Abstract While blockchain technology (BT) is considered secure, there are several vulnerabilities that can breach its security. The study in artificial intelligence (AI) and BT is widely popular due to its expanding importance in enhancing security and computational prowess. In this study, we present a comprehensive and meticulous comprehensive review of AI and BT‐based privacy‐preserving smart healthcare. The selection for this study was based on a holistic and integrated approach which involved examining not only individual studies but also their relationships, and trends. Through a systematic analysis of various phases, we identified 91 primary studies pertaining to information needed to stockpile directions called for retorting the research queries. We have undertaken a descriptive comparison of foundational manuscripts, taking into account an array of essential factors, including performance metrics, security protocols, and computational prowess. Our thorough discussions and debates have led to the identification of research gaps in the current manuscript, as well as the direction for future research. We also propose our constructive approach for the aforementioned integration, highlighting its potential benefits and implications.

Neelesh Mungoli

This paper explores the potential of using ChatGPT, a state-of-the-art conversational AI system, to enhance engagement and understanding of cryptocurrency. We first provide a comprehensive review of the existing literature on both cryptocurrency and ChatGPT. We then describe the background of cryptocurrency and the capabilities of ChatGPT. We present our methodology for collecting and preprocessing a dataset of cryptocurrency-related conversations and fine-tuning ChatGPT using reinforcement learning. Our results demonstrate the effectiveness of ChatGPT in generating contextually appropriate responses to cryptocurrency-related queries, with potential applications in areas such as customer support and education. However, we also identify challenges and limitations associated with the deployment of ChatGPT in this domain, including the need for robust data privacy measures and addressing potential biases. Our findings suggest promising directions for future research in enhancing conversational engagement and understanding of cryptocurrency through ChatGPT

A. Ezil Sam Leni, Rajendran Shankar, R. Thiagarajan, Vishal Ratansing Patil

The medical sector actively changes and implements innovative features in response to technical development and revolutions.Many of the most crucial elements in IoT-connected health services are safeguarding critical patient records from prospective attackers.As a result, BlockChain (BC) is gaining traction in the business sector owing to its large implementations.As a result, BC can efficiently handle everyday life activities as a distributed and decentralized technology.Compared to other industries, the medical sector is one of the most prominent areas where the BC network might be valuable.It generates a wide range of possibilities and probabilities in existing medical institutions.So, throughout this study, we address BC technology's widespread application and influence in modern medical systems, focusing on the critical requirements for such systems, such as trustworthiness, security, and safety.Furthermore, we built the shared ledger for blockchain-based healthcare providers for patient information, contractual between several other parties.The study's findings demonstrate the usefulness of BC technology in IoHT for keeping patient health data.The BDSA-IoHT eliminates 2.01 seconds of service delay and 1.9 seconds of processing time, enhancing efficiency by nearly 30%.

Zahra Mohammadzadeh, Marcel Ausloos, Hamid Reza Saeidnia

Purpose ChatGPT from OpenAI is an amazing example of machine learning technology. This technology has now become an important issue for high-tech plagiarism concern. Indeed, there are many concerns about using this tool, perhaps using other technologies to make ChatGPT safer. Non-fungible tokens (NFTs) may be a way out. This paper aims to discuss such an alternative. Design/methodology/approach To preventing with high-tech plagiarism created by the ChatGPT tool two ways can help schools, universities and scientific centers to prevent academic plagiarism: first, by banning ChatGPT and adjusting teaching styles, and second, by using detecting AI-produced content. In this viewpoint, the authors suggest a third way that can be a way out. Findings NFTs technology has the ability to add a non-fungibility feature to any digital object (image, text or video). Therefore, any text produced by artificial intelligence tools can be given a specific NFT code. With this work, the authors add a feature to texts produced by artificial intelligence, that is, the non-fungibility feature. Originality/value In this viewpoint, how and why NFTs may be a usefully added value in preventing acts of high-tech plagiarism on ChatGPT is discussed.

C. U. Om Kumar, Sudhakaran Gajendran, Viswaksena Balaji, A. Nhaveen · 5 authors

Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.

Zongwei Li, Dechao Kong, Yuanzheng Niu, Hong-Li Peng · 6 authors

With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting privacy of individuals, these techniques also guarantee security and dependability of data. This paper initially presents an overview of AI and blockchain, summarizing their combination along with derived privacy protection technologies. It then explores specific application scenarios in data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity methods. Moreover, the paper evaluates five critical aspects of AI-blockchain-integration privacy protection systems, including authorization management, access control, data protection, network security, and scalability. Furthermore, it analyzes the deficiencies and their actual cause, offering corresponding suggestions. This research also classifies and summarizes privacy protection techniques based on AI-blockchain application scenarios and technical schemes. In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy.

Madhuri Hiwale, Rahee Walambe, Vidyasagar Potdar, Ketan Kotecha

The unexpected and rapid spread of the COVID-19 pandemic has amplified the acceptance of remote healthcare systems such as telemedicine. Telemedicine effectively provides remote communication, better treatment recommendation, and personalized treatment on demand. It has emerged as the possible future of medicine. From a privacy perspective, secure storage, preservation, and controlled access to health data with consent are the main challenges to the effective deployment of telemedicine. It is paramount to fully overcome these challenges to integrate the telemedicine system into healthcare. In this regard, emerging technologies such as blockchain and federated learning have enormous potential to strengthen the telemedicine system. These technologies help enhance the overall healthcare standard when applied in an integrated way. The primary aim of this study is to perform a systematic literature review of previous research on privacy-preserving methods deployed with blockchain and federated learning for telemedicine. This study provides an in-depth qualitative analysis of relevant studies based on the architecture, privacy mechanisms, and machine learning methods used for data storage, access, and analytics. The survey allows the integration of blockchain and federated learning technologies with suitable privacy techniques to design a secure, trustworthy, and accurate telemedicine model with a privacy guarantee.

Genqing Bian, Wenjing Qu, Bilin Shao

COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection.

K Shruthi, A.S. Poornima

In the present medical services, the board, clinical well-being records are as electronic clinical record (EHR/EMR) frameworks.These frameworks store patients' clinical histories in a computerized design.Notwithstanding, a patient's clinical information is gained in a productive and ideal way and is demonstrated to be troublesome through these records.Powerlessness constantly prevents the well-being of the board from getting data, less use of data obtained, unmanageable protection controls, and unfortunate information resource security.In this paper, we present an effective and safe clinical information resource, the executives' framework involving Blockchain, to determine these issues.Blockchain innovation facilitates the openness of all such records by keeping a block for each patient.This paper proposes an engineering utilizing an off-chain arrangement that will empower specialists and patients to get records in a protected manner.Blockchain makes clinical records permanent and scrambles them for information honesty.Clients can notice their wwell-being records, yet just patients own the confidential key and can impart it to those they want.Smart contracts likewise help our information proprietors to deal with their information access in a permission way.The eventual outcome will be seen as a web and portable connection point to get to, identify, and guarantee high-security information handily.In this adventure, we will give deals with any consequences regarding the issues associated with clinical consideration data and the chiefs using AI and Blockchain.Removing only the imperative information from the data is possible with the use of AI.This is done using arranged estimations.At the point when this data is taken care of, the accompanying issue is information sharing and its constancy.This is where Blockchain comes into the picture.Understanding Blockchain development guarantees that data is real and trades are secure.Blockchain development could work on clinical benefits by setting patients at the point of convergence of the clinical consideration structure and extending the insurance and interoperability of prosperity data.This paper is based in a general sense on dealing with clinical benefits data the board issues using Blockchain development and including a couple of key AI components.The fundamental thought process is to bring the attributes of AI and Blockchain together.AI assumes a pivotal part in identifying lethal illnesses.Then again, Blockchain innovation can reform clinical information base interoperability and limit unapproved record admittance.This would guarantee that the touchy patient information is firmly gotten.Expects to construct a safe, ML-driven medical care executive's framework that would guarantee that the sicknesses are precisely anticipated and sorted in the beginning phase.Further, it guarantees that the prepared model channels the information and disposes of the multitude of individual subtleties of the patient and safeguards it from information holes and breaks.It drives the framework with Blockchain to get the exchanges among patients and the approved specialist.It also gives patients the adaptability to pick which specialist should see their wwell-being record and who should not.

Omid Moztarzadeh, Mohammad Jamshidi, Saleh Sargolzaei, Fatemeh Keikhaee · 7 authors

Advanced mathematical and deep learning (DL) algorithms have recently played a crucial role in diagnosing medical parameters and diseases. One of these areas that need to be more focused on is dentistry. This is why creating digital twins of dental issues in the metaverse is a practical and effective technique to benefit from the immersive characteristics of this technology and adapt the real world of dentistry to the virtual world. These technologies can create virtual facilities and environments for patients, physicians, and researchers to access a variety of medical services. Experiencing an immersive interaction between doctors and patients can be another considerable advantage of these technologies, which can dramatically improve the efficiency of the healthcare system. In addition, offering these amenities through a blockchain system enhances reliability, safety, openness, and the ability to trace data exchange. It also brings about cost savings through improved efficiencies. In this paper, a digital twin of cervical vertebral maturation (CVM), which is a critical factor in a wide range of dental surgery, within a blockchain-based metaverse platform is designed and implemented. A DL method has been used to create an automated diagnosis process for the upcoming CVM images in the proposed platform. This method includes MobileNetV2, a mobile architecture that improves the performance of mobile models in multiple tasks and benchmarks. The proposed technique of digital twinning is simple, fast, and suitable for physicians and medical specialists, as well as for adapting to the Internet of Medical Things (IoMT) due to its low latency and computing costs. One of the important contributions of the current study is to use of DL-based computer vision as a real-time measurement method so that the proposed digital twin does not require additional sensors. Furthermore, a comprehensive conceptual framework for creating digital twins of CVM based on MobileNetV2 within a blockchain ecosystem has been designed and implemented, showing the applicability and suitability of the introduced approach. The high performance of the proposed model on a collected small dataset demonstrates that low-cost deep learning can be used for diagnosis, anomaly detection, better design, and many more applications of the upcoming digital representations. In addition, this study shows how digital twins can be performed and developed for dental issues with the lowest hardware infrastructures, reducing the costs of diagnosis and treatment for patients.

Veerasathpurush Allareddy, Sankeerth Rampa, Shankar Rengasamy Venugopalan, Mohammed H. Elnagar · 7 authors

There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of orthodontics creates a deficiency in study outcomes that can be applied to the population at large. The objective of this study is to provide a narrative review of potential applications of blockchain technology and federated machine learning to improve collaborative care. We conducted a narrative review of articles published from 2018 to 2023 to provide a high level overview of blockchain technology, federated machine learning, remote monitoring, and genomics and how they can be leveraged together to establish a patient centered model of care. To strengthen the empirical framework for clinical decision making in healthcare, we suggest use of blockchain technology and integrating it with federated machine learning. There are several challenges to adoption of these technologies in the current healthcare ecosystem. Nevertheless, this may be an ideal time to explore how best we can integrate these technologies to deliver high quality personalized care. This article provides an overview of blockchain technology and federated machine learning and how they can be leveraged to initiate collaborative projects that will have the patient at the center of care.

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Health Insurance Portability and Accountability Act Regulations place a high priority on healthcare data security. In 2021, there were over 750 data breaches, and the top seven of those exposed over 193 million personal records to fraud and identity theft. Data security is the process of preventing data from being accessed by unauthorised parties and being corrupted at any point in its lifespan. Data across all apps and platforms is protected via data encryption, hashing, tokenization, and key management procedures. The security solution now in use data encryption software to successfully improve data security by converting plain text into encrypted cypher text using an algorithm (referred to as a cypher) and an encryption key. The encrypted data will be unintelligible to unauthorised individuals. With a permitted key, only that user can then decrypt the data. Yet, when data security becomes more lax, confidential data is lost because the key is so easily hackable due to the use of a single algorithm. This project offers a solution for this issue: an effective data security system that heavily relies on WEB 3.0 and smart contracts to protect data. Additionally, it offers total data protection, ensuring that a hacker is unable to alter the data in any way. The development of a framework known as WEB 3.0 includes a block chain framework to safeguard the data at the backend. Data access does not require an encryption or decryption key, thus there is no need to worry about data breaches or tampering by hackers. Thus, this system offers complete data protection for a hospital's medical records.