Transforming Healthcare: The Power of Artificial Intelligence
Keywords:
technology revolution, modern life, health care, medicine, Artificial IntelligenceAbstract
Technology has revolutionized almost every aspect of modern life, including the way we approach and receive medical care. The purpose of this literature is to demonstrate the significant contributions that technology has made and continues to make to making quality healthcare accessible and efficient for all individuals. The literature search used Google Scholar from 2017. Using some specific criteria, the results were analysed and concluded with a deductive method. Realizing the true potential benefits of AI requires cross-industry collaboration and actively engaging patients and providers. Ultimately, our research shows that while progress has been made towards achieving AI-powered healthcare decision support that improves workflows and reduces the burden on practitioners, Further development can be focused on data and infrastructure security, ethical integration and comprehensive public accessibility, regulation of AI use, and the development of transparent and accountable AI design.
References
Courtright-Lim, A. & M. Drago. 2020. Ethics of genetic testing, Medicine, 48(10), 675-679, 1357-3039.
Beavers, J., & S. Pournouri. 2019. Recent Cyber Attacks and Vulnerabilities in Medical Devices and Healthcare Institutions. In: Jahankhani, H., Kendzierskyj, S., Jamal, A., Epiphaniou, G., Al-Khateeb, H. (eds) Blockchain and Clinical Trial. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https:// doi.org/10.1007/978-3-030-11289-9_11.
Clare, C. A. 2021. Telehealth and the digital divide as a social determinant of health during the COVID-19 pandemic. Network Modeling Analysis in Health Informatics and Bioinformatics, 10(26). https://link.springer.com/article/10.1007/s13721-021-00300-y
European Parliamentary Research Service. 2019. A governance framework for algorithmic accountability and transparency. Retrieved from https://www.europarl.europa.eu/RegData/etudes/STUD/2019/624261/EPRS_STU(2019)624261_EN.pdf
Kumar, Y., A. Koul, R. Singla, & M. F. Ijaz. 2022. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing, 14, 8459–8486.
Lee, L. M. 2017. Ethics and subsequent use of electronic health record data, Journal of Biomedical Informatics, 71, 143-146, https://doi.org/10.1016/j.jbi.2017.05.022.
Omowole, A. 2021. Research shows AI is often biased. Here’s how to make algorithms work for all of us. World Economic Forum. Retrieved from https://www.weforum.org/agenda/2021/07/ai-machine-learning-bias-discrimination/
Park, D. J., M. W. Park, H. Lee et al. 2021. Development of machine learning model for diagnostic disease prediction based on laboratory tests. Sci Rep 11, 7567. https://doi.org/10.1038/s41598-021-87171-5.
Shaikh, M., A. H. Vayani, S. Akram, & N. Qamar. 2022. Open-source electronic health record systems: A systematic review of most recent advances. Health Informatics Journal, 28(2), 14604582221099828.
Subramanian, M., A. Wojtusciszyn, L. Favre, S. Boughorbel, J. Shan, K. B. Letaief, N. Pitteloud, & L. Chouchane. 2020. Precision medicine in the era of artificial intelligence: implications in chronic disease management. Journal of translational medicine, 18(1), 472. https://doi.org/10.1186/s12967-020-02658-5.
UK Statistics Authority. 2021. Ethical considerations in the use of Machine Learning for research and statistics. Retrieved from https://uksa.statisticsauthority.gov.uk/publication/ethical-considerations-in-the-use-of-machine-learning-for-research-and-statistics/pages/2/
Vayena E, A. Blasimm, & I. G. Cohen. 2018. Machine learning in medicine: Addressing ethical challenges. PLOS Medicine 15(11): e1002689. https://doi.org/10.1371/journal.pmed.1002689.
