Literature Review: Evaluating Healthcare Workers' Readiness to Adopt AI Technology
Keywords:
Artificial Intelligence, Healthcare Workers, Technology Readiness, AI AdoptionAbstract
Artificial Intelligence (AI) has immense potential to transform healthcare services. However, the readiness of healthcare workers to adopt this technology varies widely and is not yet fully understood. Objective: This literature review aims to evaluate the readiness of healthcare workers to adopt AI technology, identify the factors influencing this readiness, and explore the challenges and opportunities in the adoption process. Methods: A literature search was conducted in the PubMed, Scopus, Web of Science, and CINAHL databases for articles published in the last five years. Inclusion criteria encompassed studies on healthcare workers' readiness for AI and peer-reviewed publications. Results: The review identified several key factors influencing AI adoption readiness, including digital knowledge and skills, perceptions of AI's benefits and risks, and organizational support. Significant variations in readiness levels were found across countries and healthcare professions. Major challenges include data security, integration with existing systems, and the need for ongoing training. Ethical and regulatory considerations also play a crucial role in AI adoption. Conclusion: Despite considerable interest in AI adoption in healthcare, the readiness of healthcare workers remains varied. A comprehensive approach is required, including enhanced education, supportive policy development, and cross-sector collaboration to facilitate the effective and responsible adoption of AI. Further research is needed to explore strategies for improving readiness and the long-term impact of AI adoption in healthcare services.
References
Gulshan, V., Peng, L., Coram, M., et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402–2410. https://doi.org/10.1001/jama.2016.17216
Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69(Suppl), S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5
Jiang, F., Jiang, Y., Zhi, H., et al. (2017). Artificial intelligence in healthcare: past, present, and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
Kermany, D. S., Goldbaum, M., Cai, W., et al. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), 1122-1131. https://doi.org/10.1016/j.cell.2018.02.010
Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2), 574-582. https://doi.org/10.1148/radiol.2017162326
Maddikunta, P. K., Pham, Q. V., Prabadevi, B., et al. (2022). Industry 5.0: A survey on enabling technologies and potential applications. Journal of Industrial Information Integration, 26, 100257. https://doi.org/10.1016/j.jii.2021.100257
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://doi.org/10.1016/S2589-7500(20)30292-2
Nandi, S., Mishra, M., & Majumder, S. (2022). Usage of AI and wearable IoT devices for healthcare data: A study. Machine Learning Algorithms for Signal and Image Processing, 315–337. https://doi.org/10.1002/9781119861850.ch18
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Paul, D., Sanap, G., Shenoy, S., et al. (2021). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80-93. https://doi.org/10.1016/j.drudis.2020.10.010
Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22-28. https://doi.org/10.1177/0141076818815510
Shah, P., Kendall, F., Khozin, S., et al. (2019). Artificial intelligence and machine learning in clinical development: A translational perspective. npj Digital Medicine, 2(1), 69. https://doi.org/10.1038/s41746-019-0148-3
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7
Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719-731. https://doi.org/10.1038/s41551-018-0305-z
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