Artificial Intelligence in Healthcare: A Way Towards Innovating Healthcare Devices
Main Article Content
Abstract
Artificial intelligence is an emerging technology that has a huge influence on healthcare facilities in today's generation. Current medical facilities are widely dependent on technology. AI technology has the potential to solve different problems in the healthcare system and it is used in the diagnosis of diseases, decision-making of treatments and training of healthcare experts. This research was performed to analyse the role of AI technology in the advancement of healthcare facilities. This research also focuses on identifying the benefits of AI technology in the advancement of medical and healthcare equipment. The quantitative research design has been followed in this research to address different research questions during the research. In this research positivism research philosophy was also followed to improve the effectiveness of the study. The quantitative data collection and analysis process has been followed in this research and a survey has been performed on 51 independent people through 10 closed-ended questions. This unbiased survey helps to make decisions and the data analysis through advanced statistical methods also improves the effectiveness of the study. This research also gives insight into the role of artificial intelligence in improving medical facilities around the world. Besides, this study also focuses on the basic implications of implementing AI technology in different medical equipment.
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References
Stewart.C.,2022., AI in healthcare market size worldwide 2021-2030, Retrieved on: 29 December 2022, From: https://www.statista.com/statistics/1334826/ai-in-healthcare-market-size-worldwide/
Thilakarathne, N. N., Kagita, M. K., &Gadekallu, T. R. (2020). The role of the internet of things in health care: a systematic and comprehensive study. Available at SSRN 3690815. https://www.researchgate.net/profile/Navod-Thilakarathne/publication/344154423_The_Role_of_the_Internet_of_Things_in_Health_Care_A_Systematic_and_Comprehensive_Study/links/5f5a1d7992851c078958bac1/The-Role-of-the-Internet-of-Things-in-Health-Care-A-Systematic-and-Comprehensive-Study.pdf
Stewart. C., 2022., Global market size for artificial intelligence in healthcare in 2016, 2017 and a forecast for 2025., Retrieved on: 29 December 2022, From: https://www.statista.com/statistics/826993/health-ai-market-value-worldwide/
Thormundsson.B.,2022., AI use cases in the pharma and healthcare industry as of 2020., Retrieved on: 29 December 2022, From: https://www.statista.com/statistics/1197960/ai-pharma-healthcare-global/
Zohuri, B., &Rahmani, F. M. (2020). Artificial intelligence versus human intelligence: A new technological race. Acta Scientific Pharmaceutical Sciences (ISSN: 2581-5423), 4(5). https://www.researchgate.net/profile/Bahman-Zohuri/publication/341186285_Artificial_Intelligence_Versus_Human_Intelligence_A_New_Technological_Race/links/5ebfa8fd458515626caca8af/Artificial-Intelligence-Versus-Human-Intelligence-A-New-Technological-Race.pdf
Bennett-Daly, G., Maxwell, H., & Bridgman, H. (2022). The health needs of regionally based individuals who experience homelessness: perspectives of service providers. International Journal of Environmental Research and Public Health, 19(14), 8368.https://doi.org/10.3390/ijerph19148368
Sukamolson, S. (2007). Fundamentals of quantitative research. Language Institute Chulalongkorn University, 1(3), 1-20. https://www.researchgate.net/profile/Vihan-Moodi/post/What_are_the_characteristics_of_quantitative_research/attachment/5f3091d0ed60840001c62a27/AS%3A922776944787456%401597018576221/download/SuphatSukamolson.pdf
Meredith, J. R., Raturi, A., Amoako‐Gyampah, K., & Kaplan, B. (1989). Alternative research paradigms in operations. Journal of operations management, 8(4), 297-326.https://www.academia.edu/download/44393584/Alternative_Research_Paradigms_in_Operat20160404-11594-68bp21.pdf
Woiceshyn, J., &Daellenbach, U. (2018). Evaluating inductive vs deductive research in management studies: Implications for authors, editors, and reviewers. Qualitative research in organizations and management: An International Journal, 13(2), 183-195.https://doi.org/10.1108/QROM-06-2017-1538
Amrhein, V., Trafimow, D., & Greenland, S. (2019). Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. The American Statistician, 73(sup1), 262-270. https://www.tandfonline.com/doi/abs/10.1080/00031305.2018.1543137
Wu, H. T. (2020). Current state of nonlinear-type time–frequency analysis and applications to high-frequency biomedical signals. Current Opinion in Systems Biology, 23, 8-21. https://www.sciencedirect.com/science/article/pii/S2452310020300184
Makowski, D., Ben-Shachar, M. S., Patil, I., &Lüdecke, D. (2020). Methods and algorithms for correlation analysis in R. Journal of Open Source Software, 5(51), 2306. https://joss.theoj.org/papers/10.21105/joss.02306.pdf
Janoskova, K., &Kral, P. (2019). An in-depth analysis of the summary innovation index in the V4 countries. Journal of competitiveness, 11(2), 68. https://www.cjournal.cz/files/326.pdf
Canbolat, A. S., Bademlioglu, A. H., Arslanoglu, N. U. R. U. L. L. A. H., &Kaynakli, O. (2019). Performance optimization of absorption refrigeration systems using Taguchi, ANOVA and Grey Relational Analysis methods. Journal of Cleaner Production, 229, 874-885. https://www.sciencedirect.com/science/article/pii/S0959652619315367
D’Alberto, L., &Lucianetti, G. (2019). Misinterpretation of the Kenessey method for the determination of the runoff coefficient: a review. Hydrological Sciences Journal, 64(3), 288-296. https://www.tandfonline.com/doi/abs/10.1080/02626667.2019.1578965
Belur, J., Tompson, L., Thornton, A., & Simon, M. (2021). Interrater reliability in systematic review methodology: exploring variation in coder decision-making. Sociological methods & research, 50(2), 837-865. https://journals.sagepub.com/doi/abs/10.1177/0049124118799372
Van Hartskamp, M., Consoli, S., Verhaegh, W., Petkovic, M., & Van de Stolpe, A. (2019). Artificial intelligence in clinical health care applications. Interactive journal of medical research, 8(2), e12100. https://i-jmr.org/2019/2/e12100
Allam, Z., & Jones, D. S. (2020, February). On the coronavirus (COVID-19) outbreak and the smart city network: universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. In Healthcare (Vol. 8, No. 1, p. 46). MDPI. https://www.mdpi.com/2227-9032/8/1/46/pdf
Muehlematter, U. J., Daniore, P., &Vokinger, K. N. (2021). Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. The Lancet Digital Health, 3(3), e195-e203. https://www.sciencedirect.com/science/article/pii/S2589750020302922
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44-56. https://www.nature.com/articles/s41591-018-0300-7
Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 271.https://doi.org/10.3390/ijerph18010271
Chen, M., &Decary, M. (2020, January). Artificial intelligence in healthcare: An essential guide for health leaders. In Healthcare management forum (Vol. 33, No. 1, pp. 10-18). Sage CA: Los Angeles, CA: SAGE Publications.https://doi.org/10.1177/0840470419873123