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Mohamed AA, Flynn G, Lucke-Wold B. Blockchain-Based Applications in Neurological Surgery. World Neurosurg 2024; 191:245-253. [PMID: 39181239 DOI: 10.1016/j.wneu.2024.08.086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 08/15/2024] [Accepted: 08/16/2024] [Indexed: 08/27/2024]
Abstract
Paper-based patient records have been associated with disorganization and redundancy and thus lack of efficiency and security. The electronic health record (EHR) is an electronic record for patient health information that has alleviated many of the traditional issues associated with paper-based records. However, in the current era of the internet and with the variability of EHR systems, privacy, security, and interoperability remain challenges of the current patient health information management systems. Blockchain technologies provide an opportunity to address many of the challenges associated with current EHR systems. In addition, new frameworks have explored the utility of blockchain-based applications in addressing concerns in different medical disciplines such as neurosurgery. This review discusses these applications, including blockchain-based solutions impacting all of medicine, relating to the EHR, and directly relating to neurosurgery. This review also discusses blockchain technology and the related intricacies for appreciating the relevant frameworks, while also highlighting the challenges associated with this technology.
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Affiliation(s)
- Ali A Mohamed
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, Florida, USA; College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, USA.
| | - Garrett Flynn
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, Florida, USA
| | - Brandon Lucke-Wold
- Department of Neurosurgery, University of Florida, Gainesville, Florida, USA
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2
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Nene L, Flepisi BT, Brand SJ, Basson C, Balmith M. Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence. Clin Ther 2024; 46:e6-e14. [PMID: 38981791 DOI: 10.1016/j.clinthera.2024.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 07/11/2024]
Abstract
PURPOSE Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes. METHODS A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes. FINDINGS The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs. IMPLICATIONS AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.
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Affiliation(s)
- Linda Nene
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Brian Thabile Flepisi
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Sarel Jacobus Brand
- Center of Excellence for Pharmaceutical Sciences, Department of Pharmacology, North-West University, Potchefstroom, South Africa
| | - Charlise Basson
- Department of Physiology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Marissa Balmith
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa.
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Sahu H, Choudhari S, Chakole S. The Use of Blockchain Technology in Public Health: Lessons Learned. Cureus 2024; 16:e63198. [PMID: 39070517 PMCID: PMC11275554 DOI: 10.7759/cureus.63198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 06/26/2024] [Indexed: 07/30/2024] Open
Abstract
Blockchain is a new technology utilized to develop creative solutions in different industries, such as health care. Blockchain is a decentralized and distributed encrypted system made up of interconnected blocks containing transaction-related information that can be shared with network participants. A blockchain network is utilized in the healthcare industry to safeguard and share patient information among hospitals, pharmacies, and doctors' diagnostic labs. Blockchain applications can precisely identify serious and potentially harmful mistakes within the medical sector. The objective is to comprehensively explore the potential use, present implementations, challenges, and future possibilities of blockchain in health management systems, and to provide information to researchers, policymakers, and practitioners on how to utilize new technology to enhance data security, efficiency, decentralization of data, authenticity of data, transparency, and verifiability of data compared to conventional databases in health management systems. Key review findings for blockchain technology in public health surveillance might include enhanced data security and accessibility of data, data storage and sharing, ensuring tamper-proof records are accessed, empowering patients, and improving overall healthcare outcomes. Its immutability proves to be important for securing healthcare data. It offers a safeguard for health records and clinical trial outcomes and ensures compliance with regulatory standards. This evaluation focuses on how it has transformed data protection, improved workflows, and safe health information interchange. Despite obstacles, further study and standardization initiatives have the potential to transform health care and guarantee patient care that is resilient and trustworthy. In the present healthcare industry, blockchain technology plays an essential role in healthcare systems. It can lead to computerized processes for collecting and validating data, accurate information collected from multiple sources, and data that are fixed, transparent to misuse, and secure, with a reduced risk of digital crimes. In addition, the study provides a detailed analysis of the potential applications for including the use of blockchain technology in transforming public health surveillance.
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Affiliation(s)
- Hemlata Sahu
- Department of Community Medicine, School of Epidemiology and Public Health, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sonali Choudhari
- Department of Community Medicine, School of Epidemiology and Public Health, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Swarupa Chakole
- Department of Community Medicine, School of Epidemiology and Public Health, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Ghorashi NS, Rahimi M, Sirous R, Javan R. The Intersection of Radiology With Blockchain and Smart Contracts: A Perspective. Cureus 2023; 15:e46941. [PMID: 38021752 PMCID: PMC10640909 DOI: 10.7759/cureus.46941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2023] [Indexed: 12/01/2023] Open
Abstract
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.
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Affiliation(s)
- Nima S Ghorashi
- Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, D.C., USA
| | - Murwarit Rahimi
- Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, D.C., USA
| | - Reza Sirous
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
- Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, D.C., USA
| | - Ramin Javan
- Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, D.C., USA
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Oakley J, Worley C, Yu LU, Brooks RR, Özçelik I, Skjellum A, Obeid JS. Scrybe: A Secure Audit Trail for Clinical Trial Data Fusion. DIGITAL THREATS : RESEARCH AND PRACTICE 2023; 4:24. [PMID: 37937206 PMCID: PMC10629820 DOI: 10.1145/3491258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 09/09/2021] [Indexed: 11/09/2023]
Abstract
Clinical trials are a multi-billion dollar industry. One of the biggest challenges facing the clinical trial research community is satisfying Part 11 of Title 21 of the Code of Federal Regulations [7] and ISO 27789 [40]. These controls provide audit requirements that guarantee the reliability of the data contained in the electronic records. Context-aware smart devices and wearable IoT devices have become increasingly common in clinical trials. Electronic Data Capture (EDC) and Clinical Data Management Systems (CDMS) do not currently address the new challenges introduced using these devices. The healthcare digital threat landscape is continually evolving, and the prevalence of sensor fusion and wearable devices compounds the growing attack surface. We propose Scrybe, a permissioned blockchain, to store proof of clinical trial data provenance. We illustrate how Scrybe addresses each control and the limitations of the Ethereum-based blockchains. Finally, we provide a proof-of-concept integration with REDCap to show tamper resistance.
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Kao PY, Yang YC, Chiang WY, Hsiao JY, Cao Y, Aliper A, Ren F, Aspuru-Guzik A, Zhavoronkov A, Hsieh MH, Lin YC. Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry. J Chem Inf Model 2023. [PMID: 37171372 DOI: 10.1021/acs.jcim.3c00562] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly developing fields with lead candidate discovery in the drug development process. First, artificial intelligence has already been demonstrated to successfully accelerate conventional drug design approaches. Second, quantum computing has demonstrated promising potential in different applications, such as quantum chemistry, combinatorial optimizations, and machine learning. This article explores hybrid quantum-classical generative adversarial networks (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of a GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules. We also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of both generated molecule properties and the achieved KL divergence. However, the hybrid quantum-classical GANs still face challenges in generating unique and valid molecules compared to their classical counterparts.
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Affiliation(s)
- Po-Yu Kao
- Insilico Medicine Taiwan Ltd., Taipei 110208, Taiwan
| | - Ya-Chu Yang
- Insilico Medicine Taiwan Ltd., Taipei 110208, Taiwan
| | - Wei-Yin Chiang
- Hon Hai (Foxconn) Research Institute, Taipei 114699, Taiwan
| | - Jen-Yueh Hsiao
- Hon Hai (Foxconn) Research Institute, Taipei 114699, Taiwan
| | - Yudong Cao
- Zapata Computing, Inc., Boston, Massachusetts 02110, United States
| | - Alex Aliper
- Insilico Medicine AI Limited, Masdar City, Abu Dhabi 145748, UAE
| | - Feng Ren
- Insilico Medicine Shanghai Ltd., Shanghai 201203, China
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research, Toronto, ON M5S 1M1, Canada
| | | | - Min-Hsiu Hsieh
- Hon Hai (Foxconn) Research Institute, Taipei 114699, Taiwan
| | - Yen-Chu Lin
- Insilico Medicine Taiwan Ltd., Taipei 110208, Taiwan
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
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7
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Afaq Y, Manocha A. Blockchain and Deep Learning Integration for Various Application: A Review. JOURNAL OF COMPUTER INFORMATION SYSTEMS 2023. [DOI: 10.1080/08874417.2023.2173330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Yasir Afaq
- Lovely Professional University, Phagwara, India
| | - Ankush Manocha
- Lovely Professional University, Phagwara, India
- National Institute of Technology, Kurukshetra, India
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8
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Karger E, Kureljusic M. Artificial Intelligence for Cancer Detection-A Bibliometric Analysis and Avenues for Future Research. Curr Oncol 2023; 30:1626-1647. [PMID: 36826086 PMCID: PMC9954989 DOI: 10.3390/curroncol30020125] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/18/2023] [Accepted: 01/27/2023] [Indexed: 01/31/2023] Open
Abstract
After cardiovascular diseases, cancer is responsible for the most deaths worldwide. Detecting a cancer disease early improves the chances for healing significantly. One group of technologies that is increasingly applied for detecting cancer is artificial intelligence. Artificial intelligence has great potential to support clinicians and medical practitioners as it allows for the early detection of carcinomas. During recent years, research on artificial intelligence for cancer detection grew a lot. Within this article, we conducted a bibliometric study of the existing research dealing with the application of artificial intelligence in cancer detection. We analyzed 6450 articles on that topic that were published between 1986 and 2022. By doing so, we were able to give an overview of this research field, including its key topics, relevant outlets, institutions, and articles. Based on our findings, we developed a future research agenda that can help to advance research on artificial intelligence for cancer detection. In summary, our study is intended to serve as a platform and foundation for researchers that are interested in the potential of artificial intelligence for detecting cancer.
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Affiliation(s)
- Erik Karger
- Information Systems and Strategic IT Management, University of Duisburg-Essen, 45141 Essen, Germany
| | - Marko Kureljusic
- International Accounting, University of Duisburg-Essen, 45141 Essen, Germany
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9
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Kumar S, Lim WM, Sivarajah U, Kaur J. Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2023; 25:871-896. [PMID: 35431617 PMCID: PMC9005027 DOI: 10.1007/s10796-022-10279-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/31/2022] [Indexed: 05/09/2023]
Abstract
Artificial intelligence (AI) and blockchain are the two disruptive technologies emerging from the Fourth Industrial Revolution (IR4.0) that have introduced radical shifts in the industry. The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. Although research on the application and convergence of AI and blockchain exists, our understanding of the utility of its integration for business remains fragmented. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using bibliometric analysis, this study reveals the most influential articles on the subject based on their publications, citations, and importance in the intellectual network. Using content analysis, this study sheds light on the subject's intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The study concludes with 10 application areas in business that can benefit from these technologies.
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Affiliation(s)
- Satish Kumar
- Department of Management Studies, Malaviya National Institute of Technology, Jaipur, Rajasthan 302017 India
- Faculty of Business, Design and Arts, Swinburne University of Technology, Jalan Simpang Tiga, 93350 Kuching, Sarawak Malaysia
| | - Weng Marc Lim
- Faculty of Business, Design and Arts, Swinburne University of Technology, Jalan Simpang Tiga, 93350 Kuching, Sarawak Malaysia
- School of Business, Law and Entrepreneurship, Swinburne University of Technology, John Street, Hawthorn, Victoria 3122 Australia
| | - Uthayasankar Sivarajah
- School of Management, Faculty of Management, Law and Social Sciences, University of Bradford, Richmond Road, Bradford, BD7 1DP UK
| | - Jaspreet Kaur
- Department of Management Studies, Malaviya National Institute of Technology, Jaipur, Rajasthan 302017 India
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10
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Kumar R, Singh D, Srinivasan K, Hu YC. AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions. Healthcare (Basel) 2022; 11:healthcare11010081. [PMID: 36611541 PMCID: PMC9819078 DOI: 10.3390/healthcare11010081] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/29/2022] Open
Abstract
Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work.
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Affiliation(s)
- Ritik Kumar
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Divyangi Singh
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Yuh-Chung Hu
- Department of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26047, Taiwan
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11
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Xu Z, Xiang D, He J. Data Privacy Protection in News Crowdfunding in the Era of Artificial Intelligence. JOURNAL OF GLOBAL INFORMATION MANAGEMENT 2022. [DOI: 10.4018/jgim.286760] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
This paper aims to study the protection of data privacy in news crowdfunding in the era of artificial intelligence. This paper respectively quotes the encryption algorithm of artificial intelligence data protection and the BP neural network prediction model to analyze the data privacy protection in news crowdfunding in the artificial intelligence era. Finally, this paper also combines the questionnaire survey method to understand the public’s awareness of privacy. The results of this paper show that artificial intelligence can promote personal data awareness and privacy, improve personal data and privacy measures and methods, and improve the effectiveness and level of privacy and privacy. In the analysis, the survey found that male college students only have 81.1% of the cognition of personal trait information, only 78.5% of network trace information, and only 78.3% of female college students’ cognition of personal credit.
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Affiliation(s)
- Zhiqiang Xu
- School of Film and Animation, China-ASEAN Art College of Chengdu University & School of Digital Media and Creative Design, Sichuan College of the Communication, China & The Education University of Hong Kong, China
| | - Dong Xiang
- School of Digital Media and Creative Design, Sichuan College of Communication, China
| | - Jialiang He
- School of Information and Communication Engineering, Dalian Nationalities University, China
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12
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Yang Y. Artificial intelligence-based organizational human resource management and operation system. Front Psychol 2022; 13:962291. [PMID: 35936267 PMCID: PMC9355249 DOI: 10.3389/fpsyg.2022.962291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/05/2022] [Indexed: 12/05/2022] Open
Abstract
The trend of globalization, marketization, and informatization continues to strengthen, in today’s development environment, how to seize the opportunity and obtain a competitive advantage in human resources is an important issue that needs to be explored. Human resource management refers to the effective use of relevant human resources inside and outside the organization through management forms under the guidance of economics and humanistic thinking. It is a general term for a series of activities that ensure the achievement of organizational goals and the maximization of member development. With the rapid development of society and economy, the competition between enterprises has intensified. If an enterprise wants to adapt to social development, it is necessary to strengthen the internal management of the organization. The internal management also needs to rely on human resource management. The purpose of this paper is to study an organization’s human resource management and operation system based on artificial intelligence. It expects to use artificial intelligence technology to design the human resource management system and to improve the quality of employees to make the enterprise develop toward a more scientific and reasonable method. It uses artificial intelligence technology to mine the relevant data of enterprises, understand the situation of enterprises in a timely manner, and adjust unreasonable rules. This paper establishes a dynamic capability evaluation model and an early warning model for human resource management and further studies the improvement approach based on human resource management. This paper analyzes the application, feasibility, and practical significance of data mining technology in human resource management systems. It focuses on the commonly used algorithms in the field of data mining and proposes specific algorithm application scenarios and implementation ideas combined with the needs of human resource management practices. The experimental results of this paper show that the average working life of incumbent employees is 3.5 years, the average length of employees who leave the company is 5 years, and some employees are 5–6 years old. From this data, it can be seen that the average number of years of on-the-job employees is short, and the work experience has yet to be accumulated.
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13
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Study on the Application of Visual Communication Design in APP Interface Design in the Context of Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9262676. [PMID: 35769267 PMCID: PMC9236836 DOI: 10.1155/2022/9262676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/13/2022] [Accepted: 04/25/2022] [Indexed: 11/18/2022]
Abstract
Visual communication concepts enable linguistics or semiotics to the teaching of visual communication designs, creating graphic designs into an innovative and scientific discipline. The use of storyline techniques in visual communication not only inspires the imagination of designer but also arouses the visual memory of the audience. Besides, improving cultural heritage such as historical images is important to protect cultural diversity. Recently, the developments of deep learning (DL) and computer vision (CV) approaches make it possible for the automatic colorization of grayscale images into color images. Also, the usage of visual communication design in APP interface design has increased. With this motivation, this work introduces the enhanced deep learning-based automated historical image colorization (EDL-AHIC) technique for wireless network-enabled visual communication. The proposed EDL-AHIC technique intends to effectually convert the grayscale images into color images. The presented EDL-AHIC technique extracts the local as well as global features. For global feature extraction, the enhanced capsule network (ECN) model is applied. Finally, the fusion layer and decoding unit are employed to determine the output, i.e., chrominance component of the input image. A comprehensive experimental validation process is performed to ensure the betterment of the EDL-AHIC technique. The comparison study reported the supremacy of the EDL-AHIC technique over the other recent methods.
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14
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Scene Construction and Application of Panoramic Virtual Simulation in Interactive Dance Teaching Based on Artificial Intelligence Technology. JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2022. [DOI: 10.1155/2022/5770385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In recent years, with the rapid development of human motion simulation technology and virtual simulation technology, natural human-computer interaction has become the main form of research in the computer industry. Due to the research capabilities and practical value of mobile video technology, it is widely used in advanced fields, such as video animation production, rehabilitation medicine, sports training, and game software, effectively realizing the connection between the three-dimensional world and the simulation world. However, the application of motion imaging technology in teaching activities is not perfect. This article is based on dance teaching, adopts a dance movement analysis method based on vector matching, and uses artificial intelligence technology to create a virtual panoramic scene. Experimental data show that the average recognition rate of gestures is 89.8%, which can meet the gesture switching interaction of working conditions in virtual simulation of equipment operation.
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15
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Bhatt P, Liu J, Gong Y, Wang J, Guo Y. Emerging Artificial Intelligence–Empowered mHealth: Scoping Review. JMIR Mhealth Uhealth 2022; 10:e35053. [PMID: 35679107 PMCID: PMC9227797 DOI: 10.2196/35053] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/23/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background
Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions.
Objective
Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years.
Methods
Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as “mobile healthcare,” “wearable medical sensors,” “smartphones”, and “AI.” We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain.
Results
We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research.
Conclusions
The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.
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Affiliation(s)
- Paras Bhatt
- Department of Electrical & Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Jia Liu
- The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Yanmin Gong
- Department of Electrical & Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Jing Wang
- Florida State University, Tallahassee, FL, United States
| | - Yuanxiong Guo
- Department of Electrical & Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, United States
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Bali S, Bali V, Mohanty RP, Gaur D. Analysis of critical success factors for blockchain technology implementation in healthcare sector. BENCHMARKING-AN INTERNATIONAL JOURNAL 2022. [DOI: 10.1108/bij-07-2021-0433] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeRecently, blockchain technology (BT) has resolved healthcare data management challenges. It helps healthcare providers automate medical records and mining to aid in data sharing and making more accurate diagnoses. This paper attempts to identify the critical success factors (CSFs) for successfully implementing BT in healthcare.Design/methodology/approachThe paper is methodologically structured in four phases. The first phase leads to identifying success factors by reviewing the extant literature. In the second phase, expert opinions were solicited to authenticate the critical success factors required to implement BT in the healthcare sector. Decision Making Trial and Evaluation Laboratory (DEMATEL) method was employed to find the cause-and-effect relationship among the third phase’s critical success factors. In phase 4, the authors resort to validating the final results and findings.FindingsBased on the analysis, 21 CSFs were identified and grouped under six dimensions. After applying the DEMATEL technique, nine factors belong to the causal group, and the remaining 12 factors fall under the effect group. The top three influencing factors of blockchain technology implementation in the healthcare ecosystem are data transparency, track and traceability and government support, whereas; implementation cost was the least influential.Originality/valueThis study provides a roadmap and may facilitate healthcare professionals to overcome contemporary challenges with the help of BT.
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Jain G, Shrivastava A, Paul J, Batra R. Blockchain for SME Clusters: An Ideation using the Framework of Ostrom Commons Governance. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2022; 24:1125-1143. [PMID: 35611300 PMCID: PMC9120342 DOI: 10.1007/s10796-022-10288-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/02/2022] [Indexed: 06/15/2023]
Abstract
Small and medium-sized enterprises (SMEs) organize themselves into clusters by sharing a set of limited resources to achieve the holistic success of the cluster. However, these SMEs often face conflicts and deadlock situations that hinder the fundamental operational dynamics of the cluster due to varied reasons, including lack of trust and transparency in interactions, lack of common consensus, and lack of accountability and non-repudiation. Blockchain technology brings trust, transparency, and traceability to systems, as demonstrated by previous research and practice. In this paper, we explore the role of blockchain technology in building a trustworthy yet collaborative environment in SME clusters through the principles of community self-governance based on the work of Nobel Laureate Elinor Ostrom. We develop and present a blockchain commons governance framework for the three main dimensions i.e., interaction, autonomy, and control, based on the theoretical premise of equivalence mapping and qualitative analysis. This paper examines the role of blockchain technology to act as a guiding mechanism and support the smooth functioning of SMEs for their holistic good. The study focuses on sustainability and improving productivity of SMEs operating in clusters under public and private partnership. This is the first study to address the operational challenges faced by SEMs in clusters by highlighting the dimensions of blockchain commons governance dimensions.
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Affiliation(s)
- Geetika Jain
- Keele Business School, Keele University, Keele, UK
| | | | - Justin Paul
- Graduate School of Business Administration, University of Puerto Rico, San Juan, Puerto Rico USA
- University of Reading, Reading, United Kingdom
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Identification of Potential WSB1 Inhibitors by AlphaFold Modeling, Virtual Screening, and Molecular Dynamics Simulation Studies. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:4629392. [PMID: 35600960 PMCID: PMC9122669 DOI: 10.1155/2022/4629392] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 04/27/2022] [Indexed: 12/03/2022]
Abstract
WD40 repeat and SOCS box containing 1 (WSB1) consists of seven WD40 repeat structural domains at the N-terminal end and one SOCS box structural domain at the C-terminal end. WSB1 promotes cancer progression by affecting the Von Hippel–Lindau tumor suppressor protein (pVHL) and upregulating hypoxia inducible factor-1α (HIF-1α) target gene expression. However, the crystal structure of WSB1 has not been reported, which is not beneficial to the research on WSB1 inhibitors. Therefore, we focused on specific small molecule inhibitors of WSB1. This study applied virtual screening and molecular dynamics simulations; finally, 20 compounds were obtained. Among them, compound G490-0341 showed the best stable structure and was a promising composite for further development of WSB1 inhibitors.
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Prevention and Detection Research of Intelligent Sports Rehabilitation under the Background of Artificial Intelligence. Appl Bionics Biomech 2022; 2022:3347166. [PMID: 35572060 PMCID: PMC9095379 DOI: 10.1155/2022/3347166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 03/29/2022] [Accepted: 04/09/2022] [Indexed: 11/17/2022] Open
Abstract
Artificial intelligence can bring convenience to human life. In the field of sports rehabilitation, the application of artificial intelligence is becoming more and more in-depth. This paper is aimed at studying the prevention and detection of sports rehabilitation in the context of artificial intelligence and proposing a compliance control method for lower limb rehabilitation robots based on artificial neural networks. In this paper, a double closed-loop control system is designed: the outer loop is an adaptive impedance control model based on sEMG feedback, and the purpose is to adjust the predicted desired joint trajectories. In the inner loop, a sliding mode iterative learning controller is designed to suppress periodic disturbance and abnormal jitter and achieve stable tracking of the target trajectory. Finally, the control method is simulated and verified by matlab/simulink, and a statistical experiment is done on the patient's recovery. The experimental results show that the use of artificial intelligence technology can effectively increase the sensitivity of the control system and improve the recovery rate of patients. Compared with the traditional sports rehabilitation control system, the sensitivity is increased by 22.7%, and the patient recovery rate is increased by 10.4%, which is of great significance in the field of sports rehabilitation.
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Abstract
A blockchain, as a form of distributed ledger technology, represents the unanimity of replication, synchronization, and sharing of data among various geographical sites. Blockchains have demonstrated impressive and effective applications throughout many aspects of the business. Blockchain technology can lead to the advent of the construction of Digital Twins (DTs). DTs involve the real representation of physical devices digitally as a virtual representation of both elements and dynamics prior to the building and deployment of actual devices. DT products can be built using blockchain-based technology in order to achieve sustainability. The technology of DT is one of the emerging novel technologies of Industry 4.0, along with artificial intelligence (AI) and the Internet of Things (IoT). Therefore, the present study adopts intelligent decision-making techniques to conduct a biased analysis of the drivers, barriers, and risks involved in applying blockchain technologies to the sustainable production of DTs. The proposed model illustrates the use of neutrosophic theory to handle the uncertain conditions of real-life situations and the indeterminate cases evolved in decision-makers’ judgments and perspectives. In addition, the model applies the analysis of Multi-criteria Decision Making (MCDM) methods through the use of ordered weighted averaging (OWA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to achieve optimal rankings for DT production providers based on consistent weighted decision-maker’s judgments in order to maintain and to assure sustainability. An empirical study is applied to the uncertain environment to aid decision-makers in achieving ideal decisions for DT providers with respect to various DT challenges, promoting sustainability and determining the best service providers. The Monte Carlo simulation method is used to illustrate, predict, and forecast the importance of the weights of decision-makers' judgments as well as the direct impact on the sustainability of DT production.
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21
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Saeed H, Malik H, Bashir U, Ahmad A, Riaz S, Ilyas M, Bukhari WA, Khan MIA. Blockchain technology in healthcare: A systematic review. PLoS One 2022; 17:e0266462. [PMID: 35404955 PMCID: PMC9000089 DOI: 10.1371/journal.pone.0266462] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/21/2022] [Indexed: 11/18/2022] Open
Abstract
Blockchain technology (BCT) has emerged in the last decade and added a lot of interest in the healthcare sector. The purpose of this systematic literature review (SLR) is to explore the potential paradigm shift in healthcare utilizing BCT. The study is compiled by reviewing research articles published in nine well-reputed venues such as IEEE Xplore, ACM Digital Library, Springs Link, Scopus, Taylor & Francis, Science Direct, PsycINFO, Ovid Medline, and MDPI between January 2016 to August 2021. A total of 1,192 research studies were identified out of which 51 articles were selected based on inclusion criteria for this SLR that presents the modern information on the recent implications and gaps in the use of BCT for enhancing the healthcare procedures. According to the outcomes, BCT is being applied to design the novel and advanced interventions to enrich the current protocol of managing, distributing, and processing clinical records and personal medical information. BCT is enduring the conceptual development in the healthcare domain, where it has summed up the substantial elements through better and enhanced efficiency, technological innovation, access control, data privacy, and security. A framework is developed to address the probable field where future researchers can add considerable value, such as data protection, system architecture, and regulatory compliance. Finally, this SLR concludes that the upcoming research can support the pervasive implementation of BCT to address the critical dilemmas related to health diagnostics, enhancing the patient healthcare process in remote monitoring or emergencies, data integrity, and avoiding fraud.
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Affiliation(s)
- Huma Saeed
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Hassaan Malik
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
- Department of Computer Science, University of Management and Technology, Lahore, Pakistan
- * E-mail:
| | - Umair Bashir
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Aiesha Ahmad
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Shafia Riaz
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Maheen Ilyas
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Wajahat Anwaar Bukhari
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
| | - Muhammad Imran Ali Khan
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
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22
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Sangal S, Nigam A, Bhutani C. Conceptualizing the role of blockchain in omnichannel healthcare: a Delphi study. ASLIB J INFORM MANAG 2022. [DOI: 10.1108/ajim-08-2021-0230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
PurposeThis study aims to identify the challenges in the healthcare industry as it adopts an omnichannel setup in an emerging economy context. Further, the study determines the scope of blockchain in addressing these challenges.Design/methodology/approachThe study uses a qualitative approach to understand the challenges in the omnichannel healthcare industry and know the scope of blockchain in building an omnichannel healthcare system. In the first stage, it did an in-depth analysis of the extant literature, followed by a Delphi study with 24 healthcare experts.FindingsThe study presents the current challenges in the omnichannel healthcare sector in an emerging economy. Further, it develops a novel conceptual framework for blockchain adoption in the omnichannel healthcare industry. The study also presents propositions that will help healthcare service providers enhance decision-making concerning the adoption of blockchain in the healthcare industry.Research limitations/implicationsThe research results may lack generalizability due to the exploratory approach and emerging economies context. Theoretically, in this study, the authors extend the theory of swift trust and organization information processing theory in an omnichannel healthcare context.Practical implicationsThe propositions provided in this paper can help healthcare managers make strategic decisions on the scope of adoption of blockchain for omnichannel healthcare.Originality/valueThis study explores the understudied area of challenges in omnichannel healthcare and the scope of blockchain for omnichannel healthcare in an emerging economy context.
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23
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Kilgallon JL, Tewarie IA, Broekman MLD, Rana A, Smith TR. Passive Data Use for Ethical Digital Public Health Surveillance in a Postpandemic World. J Med Internet Res 2022; 24:e30524. [PMID: 35166676 PMCID: PMC8889482 DOI: 10.2196/30524] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 09/14/2021] [Accepted: 11/30/2021] [Indexed: 12/15/2022] Open
Abstract
There is a fundamental need to establish the most ethical and effective way of tracking disease in the postpandemic era. The ubiquity of mobile phones is generating large amounts of passive data (collected without active user participation) that can be used as a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern. Thus, officials must look to history and current moral frameworks to avoid past mistakes and ethical pitfalls. Past pandemics demonstrate that the aftermath is the most effective time to make health policy decisions. However, an ethical discussion of passive data use for digital public health surveillance has yet to be attempted, and little has been done to determine the best method to do so. Therefore, we aim to highlight four potential areas of ethical opportunity and challenge: (1) informed consent, (2) privacy, (3) equity, and (4) ownership.
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Affiliation(s)
- John L Kilgallon
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, United States.,Department of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Ishaan Ashwini Tewarie
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, United States.,Faculty of Medicine, Erasmus University Rotterdam, Rotterdam, Netherlands.,Department of Neurosurgery, Haaglanden Medical Center, The Hague, Rotterdam, Netherlands.,Department of Neurosurgery, Leiden Medical Center, Leiden, Netherlands
| | - Marike L D Broekman
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, United States.,Department of Neurosurgery, Haaglanden Medical Center, The Hague, Rotterdam, Netherlands.,Department of Neurosurgery, Leiden Medical Center, Leiden, Netherlands
| | - Aakanksha Rana
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, United States.,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Boston, MA, United States
| | - Timothy R Smith
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, United States
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24
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Martin W, Sheynkman G, Lightstone FC, Nussinov R, Cheng F. Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease. Curr Opin Struct Biol 2022; 72:103-113. [PMID: 34628220 PMCID: PMC8860862 DOI: 10.1016/j.sbi.2021.09.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/30/2021] [Accepted: 09/01/2021] [Indexed: 02/03/2023]
Abstract
The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of molecular dynamics simulations. To date, these capabilities extend from running very long simulations (tens to hundreds of microseconds) to thousands of short simulations. However, the expansive data generated in these simulations must be made interpretable not only by the investigator who performs them but also by others as well. Here, we demonstrate how integrating learning techniques, such as artificial intelligence, machine learning, and neural networks, into analysis pipelines can reveal the kinetics of Alzheimer's disease (AD) protein aggregation. We review select AD targets, describe current simulation methods, and introduce learning concepts and their application in AD, highlighting limitations and potential solutions.
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Affiliation(s)
- William Martin
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Gloria Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, 22903, USA
| | - Felice C Lightstone
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Lab, Livermore, CA, 94550, USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
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25
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PRISED tangle: a privacy-aware framework for smart healthcare data sharing using IOTA tangle. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-021-00610-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
AbstractHealthcare has evolved significantly in recent years primarily due to the advancements in and increasing adoption of technology in healthcare processes such as data collection, storage, diagnostics, and treatment. The emergence of the industrial internet of things (IIoT) has further evolved e-Health by facilitating the development of connected healthcare systems which can significantly improve data connectivity, visibility, and interoperability leading to improved quality of service delivered to patients. However, such technological advancements come with their perils—there are growing concerns with regards to the security and privacy of healthcare data especially when collected, shared, and processed using cutting-edge connected sensor devices affecting the adoption of next-generation e-healthcare systems. In particular, during the front-end and back-end data transfer in health information exchange (HIE) there exist a security risk in term of confidentiality, integrity, authentication and access control of the data due to the limited capabilities of IoT devices involved. In this paper, we investigate the use of distributed ledger technologies (DLT) to address such security concerns for emerging healthcare systems. In particular, we use masked authenticated messaging (MAM) over the Tangle to achieve secure data sharing within a healthcare system and provide a proof-of-concept of applying the proposed approach for securing healthcare data in a connected IIoT environment. Further, we have performed the evaluation and analysis of data communication against the metrics of encryption and efficiency in transaction time.
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26
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Charles WM, Delgado BM. Health Datasets as Assets: Blockchain-Based Valuation and Transaction Methods. BLOCKCHAIN IN HEALTHCARE TODAY 2022; 5:185. [PMID: 36779021 PMCID: PMC9907414 DOI: 10.30953/bhty.v5.185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/19/2021] [Accepted: 12/21/2021] [Indexed: 05/13/2023]
Abstract
There is increasing recognition about health-oriented datasets that could be regarded as intangible assets: distinct assets with future economic benefits but without physical properties. While health-oriented datasets - particularly health records - are ascribed monetary value on the black market, there are few established methods for assessing the value for legitimate research and business purposes. The emergence of blockchain has created new commercial opportunities for transferring assets without intermediaries. Therefore, blockchain is proposed as a medium by which research datasets could be transacted to provide future value. For authorized individuals to verify their transactions, blockchain methodologies offer security, auditability, and transparency. The authors share data valuation methodologies consistent with accounting principles and include discussions of black market valuation of health data. Furthermore, this article describes blockchain-based methods of managing real-time payment/micropayment strategies.
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27
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Ai R, Jin X, Tang B, Yang G, Niu Z, Fang EF. Aging and Alzheimer’s Disease. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_74] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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28
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Abounassar EM, El-Kafrawy P, Abd El-Latif AA. Security and Interoperability Issues with Internet of Things (IoT) in Healthcare Industry: A Survey. STUDIES IN BIG DATA 2022:159-189. [DOI: 10.1007/978-3-030-85428-7_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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29
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Blockchain applications for the healthcare sector: Uses beyond Bitcoin. BLOCKCHAIN APPLICATIONS FOR HEALTHCARE INFORMATICS 2022. [PMCID: PMC9212252 DOI: 10.1016/b978-0-323-90615-9.00022-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The year 2020 will always be remembered as a catastrophic year since the World Health Organization (WHO) declared COVID-19 (coronavirus) as a pandemic. The sudden emergence of the COVID-19 pandemic has exposed the drawbacks in our healthcare systems to handle public health emergencies. It is clear that implementing cutting-edge technology such as blockchain can aid in reducing the devastating effects of healthcare mishaps and promote efficient planning operations and resource deployment. In the healthcare sector, blockchain technology has the potential to improve clinical trial data management by minimizing regulatory approval delays and revitalizing communication between various parties in the supply chain, among other things. Furthermore, the propagation of misinformation has accelerated in recent years, and existing platforms lack the ability to verify the accuracy of data, resulting in public alarm and irrational behavior. As a result, building a blockchain-based tracking system is critical to ensuring that any data related to healthcare are collected in an accurate and trustworthy manner. Blockchain is a decentralized distributed ledger distribution technology that can make data processing, provenance, and authentication simpler and has the potential to disrupt healthcare. Blockchain is being actively explored in the health sector to optimize business processes, lower costs, improve patient quality, improve compliance, and make better use of data relevant to healthcare. Nevertheless, the need to ensure that blockchain design elements consider specific healthcare needs from the different viewpoints of clients, patients, providers, and regulators is crucial in determining whether blockchain will fulfill the excitement of a technology described as “revolutionary” and “disruptive.” In addition, blockchain approaches must also be attentive to the specific problems facing healthcare relative to other sectors of the economy, in response to the actual needs of healthcare stakeholders. There have been many proposed implementations of blockchain in the healthcare sector, such as electronic health record (EHR) systems. In this chapter, we review the motivations of inculcating blockchain in currently existing EHR systems. The content of this chapter is organized as follows. Section 1 discusses a brief introduction of blockchain along with the EHR system and the current implementation of blockchain in the medical industry. The second section provides a deeper insight into the importance of mobile health, remote monitoring, and the motivations for using blockchain-based EHR systems. The third section will cover the details of a patient-centered blockchain model along with its promising features. The development of the COVID-19 pandemic has created a slew of difficulties around the world. The use of blockchain technology is critical in developing a platform for effectively managing the COVID-19 pandemic. The lack of a precise mechanism to detect new infections and anticipate the coronavirus infection risk is the largest difficulty facing most nations. The numerous characteristics of blockchain technology, such as decentralization, transparency, and immutability, can aid in pandemic management by detecting breakouts early, expediting drug distribution, and safeguarding user privacy while undergoing treatment. In this chapter, we examine blockchain applications and potential in the fight against COVID-19, demonstrating that the proposed solution is cost-effective and assures data integrity, security, transparency, and traceability among stakeholders during the pandemic. The last section is the conclusion.
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30
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Business Data Sharing through Data Marketplaces: A Systematic Literature Review. JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH 2021. [DOI: 10.3390/jtaer16070180] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Data marketplaces are expected to play a crucial role in tomorrow’s data economy, but such marketplaces are seldom commercially viable. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially not of neglected research topics that may advance such marketplaces toward commercialization. This study provides an overview of the state-of-the-art of data marketplace research. We employ a Systematic Literature Review (SLR) approach to examine 133 academic articles and structure our analysis using the Service-Technology-Organization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.
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31
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Cheng ASK, Guan Q, Su Y, Zhou P, Zeng Y. Integration of Machine Learning and Blockchain Technology in the Healthcare Field: A Literature Review and Implications for Cancer Care. Asia Pac J Oncol Nurs 2021; 8:720-724. [PMID: 34790856 PMCID: PMC8522602 DOI: 10.4103/apjon.apjon-2140] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/04/2021] [Indexed: 11/04/2022] Open
Abstract
This brief report aimed to describe a narrative review about the application of machine learning (ML) methods and Blockchain technology (BCT) in the healthcare field, and to illustrate the integration of these two technologies in cancer survivorship care. A total of six eligible papers were included in the narrative review. ML and BCT are two data-driven technologies, and there is rapidly growing interest in integrating them for clinical data management and analysis in healthcare. The findings of this report indicate that both technologies can integrate feasibly and effectively. In conclusion, this brief report provided the state-of-art evidence about the integration of the most promising technologies of ML and BCT in health field, and gave an example of how to apply these two most disruptive technologies in cancer survivorship care.
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Affiliation(s)
- Andy S K Cheng
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Qiongyao Guan
- Department of Nursing, Yunnan Cancer Hospital, Kunming, China
| | - Yan Su
- Department of Nursing, Yunnan Cancer Hospital, Kunming, China
| | - Ping Zhou
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yingchun Zeng
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
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32
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Chang V, Gagnon S, Valverde R, Ramachandran M. Guest editorial. JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT 2021. [DOI: 10.1108/jeim-09-2021-555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Decentralizing science: Towards an interoperable open peer review ecosystem using blockchain. Inf Process Manag 2021. [DOI: 10.1016/j.ipm.2021.102724] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Xie Y, Zhang J, Wang H, Liu P, Liu S, Huo T, Duan YY, Dong Z, Lu L, Ye Z. Applications of Blockchain in the Medical Field: Narrative Review. J Med Internet Res 2021; 23:e28613. [PMID: 34533470 PMCID: PMC8555946 DOI: 10.2196/28613] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 08/12/2021] [Accepted: 09/10/2021] [Indexed: 02/06/2023] Open
Abstract
Background As a distributed technology, blockchain has attracted increasing attention from stakeholders in the medical industry. Although previous studies have analyzed blockchain applications from the perspectives of technology, business, or patient care, few studies have focused on actual use-case scenarios of blockchain in health care. In particular, the outbreak of COVID-19 has led to some new ideas for the application of blockchain in medical practice. Objective This paper aims to provide a systematic review of the current and projected uses of blockchain technology in health care, as well as directions for future research. In addition to the framework structure of blockchain and application scenarios, its integration with other emerging technologies in health care is discussed. Methods We searched databases such as PubMed, EMBASE, Scopus, IEEE, and Springer using a combination of terms related to blockchain and health care. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion. Through a literature review, we summarize the key medical scenarios using blockchain technology. Results We found a total of 1647 relevant studies, 60 of which were unique studies that were included in this review. These studies report a variety of uses for blockchain and their emphasis differs. According to the different technical characteristics and application scenarios of blockchain, we summarize some medical scenarios closely related to blockchain from the perspective of technical classification. Moreover, potential challenges are mentioned, including the confidentiality of privacy, the efficiency of the system, security issues, and regulatory policy. Conclusions Blockchain technology can improve health care services in a decentralized, tamper-proof, transparent, and secure manner. With the development of this technology and its integration with other emerging technologies, blockchain has the potential to offer long-term benefits. Not only can it be a mechanism to secure electronic health records, but blockchain also provides a powerful tool that can empower users to control their own health data, enabling a foolproof health data history and establishing medical responsibility.
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Affiliation(s)
- Yi Xie
- Department of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiayao Zhang
- Department of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Honglin Wang
- Department of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pengran Liu
- Department of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Songxiang Liu
- Department of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tongtong Huo
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Yu-Yu Duan
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei University of Chinese Medicine, Wuhan, China
| | - Zhe Dong
- Wuhan Academy of Intelligent Medicine, Wuhan, China
| | - Lin Lu
- Department of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhewei Ye
- Department of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Use of Blockchain Technology for Electronic Prescriptions. BLOCKCHAIN IN HEALTHCARE TODAY 2021; 4:183. [PMID: 36777487 PMCID: PMC9907402 DOI: 10.30953/bhty.v4.183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/23/2021] [Accepted: 09/11/2021] [Indexed: 11/17/2022]
Abstract
Objective Distributed ledger technology can be used as a transparent, shareable ledger, that can record transactions between two parties efficiently and in a more secure, verifiable, and permanent way than the current electronic prescribing systems. We studied the use of a distributed ledger electronic prescribing programme, Prescription Abuse Greatly Reduced (PAGR) Prescriptions, to examine the effect of blockchain on provider prescribing efficiency at three family medicine clinics. Design The PAGR was installed side-by-side to the electronic health record at three family medicine practice clinics in middle Tennessee. A prospective, convenience sample of patients at all three clinics was used for analysis. Trained observers were used in each clinic to document the side-by-side use of current prescribing practice versus the use of the PAGR electronic prescribing system by the individual providers.The primary outcome was total time to write the prescription. Secondary metrics included compliance with checking the state's Physician Drug Monitoring Program (PDMP.) , accuracy of medicine reconciliation, use of patient's eligibility on insurance, prescription benefits, and change in prescription caused by benefits analysis or drug-interactions. Provider satisfaction was measure on a 4-point Likert scale.Data were analysed using two-tailed, paired Student T-tests with alpha set at 0.05. A sample size of 107 patients was calculated to have a power of 80% to detect a 50% change in the prescription writing time. Results The primary outcome of total prescription writing time was 171 ± 41 sec for current prescribing practice versus 63 ± 15 sec for the PAGR system (p = 0.0006). All providers were extremely satisfied with the use of the PAGR programme. Conclusion Use of the PAGR electronic prescription programme significantly saved a mean of 1 min 48 sec per written prescription at the three Family Medicine Clinics. The PAGR also provided accurate medicine reconciliation and complete PDMP checks for controlled substance prescriptions. The patient real-time benefits check and drug-drug and allergy-drug reviews resulted in the provider changing the prescription 28% of the time, enhancing safety and out-of-pocket patient expenses. Future enhancements include expanding the insurance benefits analysis and developing provider notifications when patients are non-compliant with filling their prescriptions.
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Orhan K, Bayrakdar IS, Celik O, Ayan B, Polat E. Can the blockchain-enabled interplanetary file system (Block-IPFS) be a solution for securely transferring imaging data for artificial intelligence research in oral and maxillofacial radiology? Imaging Sci Dent 2021; 51:337. [PMID: 34621663 PMCID: PMC8479440 DOI: 10.5624/isd.20210144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 06/20/2021] [Accepted: 06/26/2021] [Indexed: 11/18/2022] Open
Affiliation(s)
- Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
| | - Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey.,Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskişehir, Turkey
| | - Ozer Celik
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskişehir, Turkey.,Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskişehir, Turkey
| | - Buğra Ayan
- Department of Information Systems, Gazi University, Ankara, Turkey
| | - Enes Polat
- Türk Telekomünikasyon A.Ş., Ankara, Turkey
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Tagde P, Tagde S, Bhattacharya T, Tagde P, Chopra H, Akter R, Kaushik D, Rahman MH. Blockchain and artificial intelligence technology in e-Health. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:52810-52831. [PMID: 34476701 PMCID: PMC8412875 DOI: 10.1007/s11356-021-16223-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/24/2021] [Indexed: 05/21/2023]
Abstract
Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.
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Affiliation(s)
- Priti Tagde
- Bhabha Pharmacy Research Institute, Bhabha University Bhopal, Bhopal M.P, India.
- PRISAL Foundation (Pharmaceutical Royal International Society), New delhi, India.
| | - Sandeep Tagde
- PRISAL Foundation (Pharmaceutical Royal International Society), New delhi, India
| | - Tanima Bhattacharya
- School of Chemistry & Chemical Engineering, Hubei University, Wuhan, China
- Department of Science & Engineering, Novel Global Community Education Foundation, Hebersham, Australia
| | - Pooja Tagde
- Practice of Medicine Department, Govt. Homeopathy College, Bhopal, M.P, India
| | - Hitesh Chopra
- Chitkara College of Pharmacy, Rajpura, Punjab, 140401, India
| | - Rokeya Akter
- Department of Pharmacy, Jagannath University, Sadarghat, Dhaka, 1100, Bangladesh
| | - Deepak Kaushik
- Department of Pharmaceutical Sciences, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Md Habibur Rahman
- Department of Pharmacy, Southeast University, Banani, Dhaka, 1213, Bangladesh.
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Gonzales A, Smith SR, Dullabh P, Hovey L, Heaney-Huls K, Robichaud M, Boodoo R. Potential Uses of Blockchain Technology for Outcomes Research on Opioids. JMIR Med Inform 2021; 9:e16293. [PMID: 34448721 PMCID: PMC8433945 DOI: 10.2196/16293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 03/11/2021] [Accepted: 03/26/2021] [Indexed: 01/30/2023] Open
Abstract
The scale and severity of the opioid epidemic call for innovative, multipronged solutions. Research and development is key to accelerate the discovery and evaluation of interventions that support pain and substance use disorder management. In parallel, the use and integration of blockchain technology within research networks holds the potential to address some of the unique challenges facing opioid research. This paper discusses the applications of blockchain technology and illustrates potential ways in which it could be applied to strengthen the validity of outcomes research on the opioid epidemic. We reviewed published and gray literature to identify useful applications of blockchain, specifically those that address the challenges faced by opioid research networks and programs. We then convened a panel of experts to discuss the strengths, limitations, and feasibility of each application. Blockchain has the potential to address some of the issues surrounding health data management, including data availability, data sharing and interoperability, and privacy and security. We identified five primary applications of blockchain to opioids: clinical trials and pharmaceutical research, incentivizing data donation and behavior change, secure exchange and management of e-prescriptions, supply chain management, and secondary use of clinical data for research and public health surveillance. The published literature was limited, leading us to rely on gray literature, which was also limited in its discussion of the technical aspects of implementation. The technical expert panel provided additional context and an assessment of feasibility that was lacking in the literature. Research on opioid use and misuse is challenging because of disparate data stored across different systems, data and system interoperability issues, and legal requirements. These areas must be navigated to make data accessible, timely, and useful to researchers. Blockchain technologies have the potential to act as a facilitator in this process, offering a more efficient, secure, and privacy-preserving solution for data exchange. Among the 5 primary applications, we found that clinical trial research, supply chain management, and secondary use of data had the most examples in practice and the potential effectiveness of blockchain. More discussions and studies should focus on addressing technical questions concerning scalability and tackling practical concerns such as cost, standards, and governance around the implementation of blockchain in health care. Policy concerns related to balancing the need for data accessibility that also protects patient privacy and autonomy in revoking consent should also be examined.
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Affiliation(s)
- Aldren Gonzales
- US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, Office of Health Policy, Washington, DC, United States
| | - Scott R Smith
- US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, Office of Health Policy, Washington, DC, United States
| | | | - Lauren Hovey
- NORC at the University of Chicago, Chicago, IL, United States
| | | | | | - Roger Boodoo
- Department of Defense, Defense Health Agency, United States Navy, Falls Church, VA, United States
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Uddin M, Salah K, Jayaraman R, Pesic S, Ellahham S. Blockchain for drug traceability: Architectures and open challenges. Health Informatics J 2021; 27:14604582211011228. [PMID: 33899576 DOI: 10.1177/14604582211011228] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Pharmaceutical supply chain (PSC) consists of multiple stakeholders including raw material suppliers, manufacturers, distributors, regulatory authorities, pharmacies, hospitals, and patients. The complexity of product and transaction flows in PSC requires an effective traceability system to determine the current and all previous product ownerships. In addition, digitizing track and trace process provides significant benefit for regulatory oversight and ensures product safety. Blockchain-based drug traceability offers a potential solution to create a distributed shared data platform for an immutable, trustworthy, accountable and transparent system in the PSC. In this paper, we present an overview of product traceability issues in the PSC and envisage how blockchain technology can provide effective provenance, track and trace solution to mitigate counterfeit medications. We propose two potential blockchain based decentralized architectures, Hyperledger Fabric and Besu to meet critical requirements for drug traceability such as privacy, trust, transparency, security, authorization and authentication, and scalability. We propose, discuss, and compare two potential blockchain architectures for drug traceability. We identify and discuss several open research challenges related to the application of blockchain technology for drug traceability. The proposed blockchain architectures provide a valuable roadmap for Health Informatics researchers to build and deploy an end-to-end solution for the pharmaceutical industry.
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Cho S, Lee K(K, Cheong A, No WG, Vasarhelyi MA. Chain of Values: Examining the Economic Impacts of Blockchain on the Value-Added Tax System. J MANAGE INFORM SYST 2021. [DOI: 10.1080/07421222.2021.1912912] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Soohyun Cho
- Department of Accounting and Information Systems, Rutgers Business School, Rutgers, The State University of New Jersey, Newark, NJ, USA
| | - Kyungha (Kari) Lee
- Department of Accounting and Information Systems, Rutgers Business School, Rutgers, The State University of New Jersey, Newark, NJ, USA
| | - Arion Cheong
- Department of Accounting, College of Business and Economics, California State University Fullerton, Fullerton, CA, USA
| | - Won Gyun No
- Department of Accounting and Information Systems, Rutgers Business School, Rutgers, The State University of New Jersey, Newark, NJ, USA
| | - Miklos A. Vasarhelyi
- Department of Accounting and Information Systems, Rutgers Business School, Rutgers, The State University of New Jersey, Newark, NJ, USA
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Chen F, Wan H, Cai H, Cheng G. Machine learning in/for blockchain: Future and challenges. CAN J STAT 2021. [DOI: 10.1002/cjs.11623] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Fang Chen
- Department of Industrial Engineering Purdue University West Lafayette IN U.S.A
| | - Hong Wan
- Department of Industrial and System Engineering North Carolina State University Raleigh NC U.S.A
| | - Hua Cai
- Department of Industrial Engineering Purdue University West Lafayette IN U.S.A
| | - Guang Cheng
- Department of Statistics Purdue University West Lafayette IN U.S.A
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Hickman CFL, Alshubbar H, Chambost J, Jacques C, Pena CA, Drakeley A, Freour T. Data sharing: using blockchain and decentralized data technologies to unlock the potential of artificial intelligence: What can assisted reproduction learn from other areas of medicine? Fertil Steril 2021; 114:927-933. [PMID: 33160515 DOI: 10.1016/j.fertnstert.2020.09.160] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 09/25/2020] [Indexed: 01/30/2023]
Abstract
The extension of blockchain use for nonfinancial domains has revealed opportunities to the health care sector that answer the need for efficient and effective data and information exchanges in a secure and transparent manner. Blockchain is relatively novel in health care and particularly for data analytics, although there are examples of improvements achieved. We provide a systematic review of blockchain uses within the health care industry, with a particular focus on the in vitro fertilization (IVF) field. Blockchain technology in the fertility sector, including data sharing collaborations compliant with ethical data handling within confines of international law, allows for large-scale prospective cohort studies to proceed at an international scale. Other opportunities include gamete donation and matching, consent sharing, and shared resources between different clinics.
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Affiliation(s)
- Cristina Fontes Lindemann Hickman
- Apricity, Paris, France; Institute of Reproduction and Developmental Biology, Imperial College London, London, United Kingdom; TMRW Life Sciences, New York, New York
| | - Hoor Alshubbar
- Apricity, Paris, France; Institute of Reproduction and Developmental Biology, Imperial College London, London, United Kingdom
| | | | | | | | - Andrew Drakeley
- Hewitt Fertility Centre, Liverpool Women's Hospital, Liverpool, United Kingdom
| | - Thomas Freour
- Service de Médecine et Biologie de la Reproduction, CHU de Nantes, Nantes, France; Centre de Recherche en Transplantation et Immunologie, INSERM, Université de Nantes, Nantes, France
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Deebak BD, AL-Turjman F. Privacy-preserving in smart contracts using blockchain and artificial intelligence for cyber risk measurements. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2021. [DOI: 10.1016/j.jisa.2021.102749] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Could Blockchain Technology Empower Patients, Improve Education, and Boost Research in Radiology Departments? An Open Question for Future Applications. J Digit Imaging 2021; 32:1112-1115. [PMID: 31197561 DOI: 10.1007/s10278-019-00246-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Blockchain can be considered as a digital database of cryptographically validated transactions stored as blocks of data. Copies of the database are distributed on a peer-to-peer network adhering to a consensus protocol for authentication of new blocks into the chain. While confined to financial applications in the past, this technology is quickly becoming a hot topic in healthcare and scientific research. Potential applications in radiology range from upgraded monitoring of training milestones achievement for residents to improved control of clinical imaging data and easier creation of secure shared databases.
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Sánchez López JD, Cambil Martín J, Luque Martínez F. [Blockchain. A new approach in patient safety]. J Healthc Qual Res 2021; 37:193-195. [PMID: 33642200 DOI: 10.1016/j.jhqr.2020.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 03/24/2020] [Accepted: 03/26/2020] [Indexed: 11/27/2022]
Affiliation(s)
- J D Sánchez López
- Facultativo Especialista de Área de Cirugía Oral y Maxilofacial, Hospital Universitario Virgen de las Nieves, Granada, España; Presidente del Comité Ético de Investigación de Granada, Granada, España.
| | - J Cambil Martín
- Doctor en Enfermería, Profesor del Departamento de Enfermería, Facultad de Ciencias de la Salud, Universidad de Granada, Granada, España
| | - F Luque Martínez
- Doctor en Farmacia, Responsable de Formación, Hospital Universitario Virgen de las Nieves, Granada, España; Vicepresidente del Comité Ético de Investigación de Granada, Granada, España
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Alves D, Yamada DB, Bernardi FA, Carvalho I, Filho MEC, Neiva MB, Lima VC, Félix TM. Mapping, Infrastructure, and Data Analysis for the Brazilian Network of Rare Diseases: Protocol for the RARASnet Observational Cohort Study. JMIR Res Protoc 2021; 10:e24826. [PMID: 33480849 PMCID: PMC7864771 DOI: 10.2196/24826] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 12/10/2020] [Accepted: 12/15/2020] [Indexed: 01/12/2023] Open
Abstract
Background A rare disease is a medical condition with low prevalence in the general population, but these can collectively affect up to 10% of the population. Thus, rare diseases have a significant impact on the health care system, and health professionals must be familiar with their diagnosis, management, and treatment. Objective This paper aims to provide health indicators regarding the rare diseases in Brazil and to create a network of reference centers with health professionals from different regions of the country. RARASnet proposes to map, analyze, and communicate all the data regarding the infrastructure of the centers and the patients’ progress or needs. The focus of the proposed study is to provide all the technical infrastructure and analysis, following the World Health Organization and the Brazilian Ministry of Health guidelines. Methods To build this digitized system, we will provide a security framework to assure the privacy and protection of each patient when collecting data. Systems development life cycle methodologies will also be applied to align software development, infrastructure operation, and quality assurance. After data collection of all information designed by the specialists, the computational analysis, modeling, and results will be communicated in scientific research papers and a digital health observatory. Results The project has several activities, and it is in an initial stage. Initially, a survey was given to all health care centers to understand the technical aspects of each network member, such as the existence of computers, technical support staff, and digitized systems. In this survey, we detected that 59% (23/39) of participating health units have electronic medical records, while 41% (16/39) have paper records. Therefore, we will have different strategies to access the data from each center in the data collection phase. Later, we will standardize and analyze the clinical and epidemiological data and use these data to develop a national network for monitoring rare diseases and a digital health observatory to make the information available. The project had its financing approved in December 2019. Retrospective data collection started in October 2020, and we expect to finish in January 2021. During the third quarter of 2020, we enrolled 40 health institutions from all regions of Brazil. Conclusions The nature of rare disease diagnosis is complex and diverse, and many problems will be faced in the evolution of the project. However, decisions based on data analysis are the best option for the improvement of the rare disease network in Brazil. The creation of RARASnet, along with all the digitized infrastructure, can improve the accessibility of information and standardization of rare diseases in the country. International Registered Report Identifier (IRRID) DERR1-10.2196/24826
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Affiliation(s)
- Domingos Alves
- Department of Social Medicine, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Diego Bettiol Yamada
- Public Health Postgraduate Program, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Filipe Andrade Bernardi
- Bioengineering Postgraduate Program, School of Engineering, University of São Paulo, São Carlos, Brazil.,Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Isabelle Carvalho
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil
| | - Márcio Eloi Colombo Filho
- Bioengineering Postgraduate Program, School of Engineering, University of São Paulo, São Carlos, Brazil
| | - Mariane Barros Neiva
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil
| | - Vinícius Costa Lima
- Bioengineering Postgraduate Program, School of Engineering, University of São Paulo, São Carlos, Brazil.,Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Têmis Maria Félix
- Medical Genetics Service, Porto Alegre Clinical Hospital, Porto Alegre, Brazil
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Research on the Application of Blockchain in Smart Healthcare: Constructing a Hierarchical Framework. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6698122. [PMID: 33505644 PMCID: PMC7815389 DOI: 10.1155/2021/6698122] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 12/18/2020] [Accepted: 12/28/2020] [Indexed: 11/22/2022]
Abstract
This study aims to explore the application of blockchain technology in smart healthcare, establish a hierarchical theoretical framework of smart healthcare, reveal the impact of blockchain on smart healthcare, and finally, construct a development application system of smart healthcare under the blockchain based on stakeholder theory. However, such a hierarchical theoretical framework should consider not only the necessary attributes and the interrelationship among various aspects and attributes but also the role of multiple stakeholders. Therefore, the paper uses fuzzy set theory to filter unnecessary attributes, proposes a decision-making and experimental evaluation laboratory (DEMATEL) to manage the complex interrelationships between various aspects and attributes, and uses Interpretive Structure Modeling (ISM) to divide the hierarchy and construct a hierarchical theoretical framework. The results show that (1) the top-level design, the medical record management, and the doctor management are the root causes of system. (2) The specific application of blockchain in the field of smart healthcare is mainly carried out around the intelligent contract, which relies on the medical record management and is constrained by the system, and optimization of application is the key to system upgrading. (3) The internal and external regulation, the medical insurance, and the environmental governance play a guaranteed role for the development of the system and effectively safeguard the interests of stakeholders. (4) The application system of smart healthcare under the blockchain needs to be built based on three layers: the transaction layer, information layer, and stakeholder layer. The theoretical hierarchical framework is intended to guide smart healthcare towards blockchain applications, and stakeholders are suggested to participate in the development application systems.
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AlShamsi M, Salloum SA, Alshurideh M, Abdallah S. Artificial Intelligence and Blockchain for Transparency in Governance. ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE DEVELOPMENT: THEORY, PRACTICE AND FUTURE APPLICATIONS 2021:219-230. [DOI: 10.1007/978-3-030-51920-9_11] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic. SUSTAINABLE OPERATIONS AND COMPUTERS 2021; 2. [PMCID: PMC8052508 DOI: 10.1016/j.susoc.2021.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Background and aims Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart. Methods We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure. Results AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed. Conclusions Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.
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Ai R, Jin X, Tang B, Yang G, Niu Z, Fang EF. Ageing and Alzheimer’s Disease. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_74-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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