A Bibliometric Analysis of Artificial Intelligence during COVID-19 Based on WOS Data Abdelmageed Algamdi

Main Article Content

Abdelmageed Algamdi

Abstract

This article opens up a new field of research in Light of COVID-19 Artificial Intelligence, mainly explaining this binding domain's current trends and knowledge fields.


       The bibliometric analysis was performed to present new research trends in Artificial Intelligence in light of COVID-19. The data of 1635 studies published in Web of Science were analyzed during the last two years (2020-2021) using three software CiteSpace, VOSviewer, and KnowledgeMatrix Plus.


      The findings suggest that there are twelve research clusters in this topic (emerging industry, cross-sectional survey study, emerging technologies, joint position paper, colony predation algorithm, medical worker, deep learning, covid-19 risk prediction, future smart connected communities, supply chain resilience, virtual screening, and k-12 students). The United States, People's Republic of China, the United Kingdom, India, Saudi Arabia, Italy, Australia, Spain, South Korea, and Canada are the most intriguing countries that investigated this issue during COVID-19, so this study reveals the latest policy trends in Artificial intelligence using bibliometric analysis

Article Details

How to Cite
Algamdi, A. (2022). A Bibliometric Analysis of Artificial Intelligence during COVID-19 Based on WOS Data Abdelmageed Algamdi . Finance and Business Economies Review, 6(1), 383–397. https://doi.org/10.58205/fber.v6i1.1563
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References

Albahri, O. S., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., Abdulkareem, K. H., Al-Qaysi, Z. T., ... & Rashid, N. A. (2020). A systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions, and methodological aspects. Journal of infection and public health.

Abd-Alrazaq, A., Schneider, J., Mifsud, B., Alam, T., Househ, M., Hamdi, M., & Shah, Z. (2021). A Comprehensive Overview of the COVID-19 Literature: Machine Learning–Based Bibliometric Analysis. Journal of medical Internet research, 23(3), e23703.

Adadi, A., Lahmer, M., & Nasiri, S. (2021). Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead. Journal of King Saud University-Computer and Information Sciences.

Agrawal, R., Taylor, Group, F., Chatterjee, J. M., Kumar, A., Rathore, P. S., & Le, D. N. (2020). Machine Learning for Healthcare: Handling and Managing Data: Taylor & Francis Limited.

Aizenberg, I., Aizenberg, N. N., & Vandewalle, J. P. (2013). Multi-valued and universal binary neurons: theory, learning, and applications: Springer Science & Business Media.

Baldi, P. (2018). Deep learning in biomedical data science. Annual review of biomedical data science, 1, 181-205.

Basiri, M. E., Abdar, M., Cifci, M. A., Nemati, S., & Acharya, U. R. (2020). A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques. Knowledge-Based Systems, 105949.

Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. Jama, 319(13), 1317-1318.

Bohr, A., & Memarzadeh, K. (2020). Artificial Intelligence in Healthcare: Elsevier Science.

Bote-Curiel, L., Munoz-Romero, S., Gerrero-Curieses, A., & Rojo-Álvarez, J. L. (2019). Deep learning and big data in Healthcare: A double review for critical beginners. Applied Sciences, 9(11), 2331.

Campion, F. X., Carlsson, G., & Francis, F. (2017). Machine Intelligence for Healthcare: CreateSpace Independent Publishing Platform.

Catania, L. J. (2020). Foundations of Artificial Intelligence in Healthcare and Bioscience: A User-Friendly Guide for IT Professionals, Healthcare Providers, Researchers, and Clinicians: Elsevier Science.

Chen, Y. W., & Jain, L. C. (2019). Deep Learning in Healthcare: Paradigms and Applications: Springer International Publishing.

Chen, C. (2016). CiteSpace: a practical guide for mapping scientific literature (pp. 41-44). Hauppauge, NY: Nova Science Publishers.

Cheon, S., Kim, J., & Lim, J. (2019). The use of deep learning to predict stroke patient mortality. International journal of environmental research and public health, 16(11), 1876.

Davis, R. (2020). “Integrating Digital Technologies and Data-driven Telemedicine into Smart Healthcare during the COVID-19 Pandemic,” American Journal of Medical Research 7(2): 22–28. doi:10.22381/AJMR7220203

Dash, S., Acharya, B. R., Mittal, M., Abraham, A., & Kelemen, A. (2019). Deep Learning Techniques for Biomedical and Health Informatics: Springer International Publishing.

Dechter, R. (1986). Learning while searching in constraint-satisfaction problems.

De Felice, F., & Polimeni, A. (2020). Coronavirus Disease (COVID-19): A Machine learning bibliometric analysis. in vivo, 34(3 suppl), 1613-1617.

Dobson-Lohman, E., and Potcovaru, A.-M. (2020). “Fake News Content Shaping the COVID-19 pandemic Fear: Virus Anxiety, Emotional Contagion, and Responsible Media Reporting,” Analysis and Metaphysics 19: 94–100. doi:10.22381/AM19202011

Durana, P., Valaskova, K., Vagner, L., Zadnanova, S., Podhorska, I. & Siekelova, A. (2020).Disclosure of Strategic Managers’ Factotum: Behavioral Incentives of Innovative Business. International Journal of Financial Studies, 8(1), 17. https://doi.org/10.3390/ijfs810017

Eastman, E. M. (2019). Deep learning models for the perception of human social interactions. Massachusetts Institute of Technology.

Faes, L., Wagner, S. K., Fu, D. J., Liu, X., Korot, E., Ledsam, J. R., . . . Kern, C. (2019). Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. The Lancet Digital Health, 1(5), e232-e242.

Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer methods and programs in biomedicine, 161, 1-13.

Georgescu, A. L., Koehler, J. C., Weiske, J., Vogeley, K., Koutsouleris, N., & Falter-Wagner, C. (2020). Machine Learning to Study Social Interaction Difficulties in ASD. Computational Approaches for Human-Human and Human-Robot Social Interactions.

Guo, X., Polanía, L. F., Garcia-Frias, J., & Barner, K. E. (2019). Social relationship recognition based on a hybrid deep neural network. Paper presented at the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019).

Hassanien, A. E., Azar, A. T., Gaber, T., Bhatnagar, R., & Tolba, M. F. (2019). The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019): Springer International Publishing.

Hossain, M. M., McKyer, E. L. J., & Ma, P. (2020). Applications of artificial intelligence technologies on mental health research during COVID-19.

Jain, V., & Chatterjee, J. M. (2020). Machine Learning with Health Care Perspective: Machine Learning and Healthcare: Springer International Publishing.

Khan, M., Mehran, M. T., Haq, Z. U., Ullah, Z., & Naqvi, S. R. (2021). Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. Expert Systems with Applications, 115695.

Kim, J., Lee, D., & Park, E. (2021). Machine Learning for Mental Health in Social Media: Bibliometric Study. Journal of Medical Internet Research, 23(3), e24870.

KISTI. (2016). KnowledgeMatrix Plus ver.0.80 for supporting Scientometric Network Analysis: Department of Scientometric Research, Korea

Institute of Science and Technology Information (KISTI).

Kwon, J.-m., Cho, Y., Jeon, K.-H., Cho, S., Kim, K.-H., Baek, S. D., . . . Oh, B.-H. (2020). A deep learning algorithm to detect anemia with ECGs: a retrospective, multicentre study. The Lancet Digital Health, 2(7), e358-e367.

Lancet, T. (2017). Artificial Intelligence in Healthcare: Within Touching Distance: Elsevier.

Lawry, T. (2020). Artificial Intelligence in Healthcare: A Leader's Guide to Winning in the New Age of Intelligent Health Systems: Taylor & Francis.

Lee, Y., Kwon, J.-m., Lee, Y., Park, H., Cho, H., & Park, J. (2018). Deep learning in the medical domain: predicting cardiac arrest using deep learning. Acute and critical care, 33(3), 117.

Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., . . . Kern, C. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271-e297.

Lyons, N., and Lăzăroiu, G. (2020). “Addressing the COVID-19 Crisis by Harnessing the Internet of Things Sensors and Machine Learning Algorithms in Data-driven Smart Sustainable Cities,”Geopolitics, History, and International Relations 12(2): 65–71. doi:10.22381/GHIR12220209

Mahajan, P. S. (2018). Artificial Intelligence in Healthcare: HARPERCOLLINS 360.

Medicine, J. (2019). Artificial Intelligence and Machine Learning for Business: Approach for Beginners to AI and Machine Learning and Their Revolution of Modern Life, Health Care, Business and Marketing: Independently Published.

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for Healthcare: review, opportunities and challenges. Briefings in bioinformatics, 19(6), 1236-1246.

Mohanty, S. N., Nalinipriya, G., Jena, O. P., & Sarkar, A. (2021). Machine Learning for Healthcare Applications: Wiley.

Naseem, M., Akhund, R., Arshad, H., & Ibrahim, M. T. (2020). Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review. Journal of Primary Care & Community Health, 11, 2150132720963634.

Ngiam, K. Y., & Khor, W. (2019). Big data and machine learning algorithms for health-care delivery. The Lancet Oncology, 20(5), e262-e273.

Nielsen, A. (2017). AI and Machine Learning for Healthcare: An Overview of Tools and Challenges for Building a Health-tech Data Pipeline: O'Reilly Media.

Nordlinger, B., Villani, C., & Rus, D. (2020). Healthcare and Artificial Intelligence: Springer International Publishing.

Panesar, A. (2019). Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes: Apress.

Purushotham, S., Meng, C., Che, Z., & Liu, Y. (2018). Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics, 83, 112-134.

Rafailidis, D., & Crestani, F. (2017). Recommendation with social relationships via deep learning. Paper presented at the Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval.

Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2016). Deep learning for health informatics. IEEE journal of biomedical and health informatics, 21(1), 4-21.

Raza, K. (2020). Artificial intelligence against COVID-19: A meta-analysis of current research. Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach, 165-176.

Saba, L., Biswas, M., Kuppili, V., Godia, E. C., Suri, H. S., Edla, D. R., . . . Mavrogeni, S. (2019). The present and future of deep learning in radiology. European journal of radiology, 114, 14-24.

Santosh, K. C., Das, N., & Ghosh, S. (2021). Deep Learning Models for Medical Imaging: Elsevier Science.

Sisodia, D. S., Pachori, R. B., & Garg, L. (2020). Advancement of Artificial Intelligence in Healthcare Engineering: Medical Information Science Reference.

Solutions, E. L. (2018). Machine Learning for Healthcare Analytics Projects: Build smart AI applications using neural network methodologies across the healthcare vertical market: Packt Publishing.

Van Eck, N. J., & Waltman, L. (2013). VOSviewer manual. Leiden: Univeristeit Leiden, 1(1), 1-53.

Yang, H.-C., Islam, M. M., & Li, Y.-C. (2018). Potentiality of deep learning application in Healthcare. Comput. Methods Programs Biomed., 161, 1.

Yazhini, K., & Loganathan, D. (2019). A state of art approaches on deep learning models in Healthcare: An application perspective. Paper presented at the 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI).

Yoon, H. (2018). New Statistical Transfer Learning Models for Health Care Applications: Arizona State University.

Yu, Y., Li, M., Liu, L., Li, Y., & Wang, J. (2019). Clinical big data and deep learning: Applications, challenges, and future outlooks. Big Data Mining and Analytics, 2(4), 288-305.

Zhang, L., Huang, J., & Liu, L. (2019). Improved deep learning network based in combination with cost-sensitive learning for early detection of ovarian cancer in color ultrasound detecting system. Journal of medical systems, 43(8), 251.

Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472.