Exploring the Acceptance of Artificial Intelligence in Healthcare in Saudi Arabia

Authors

  • Haytham Althubaiti National University of Singapore, King Fahd University of Petroleum and Minerals
  • Ali Sulaiman A. Al Yousef National University of Singapore

DOI:

https://doi.org/10.56705/ijaimi.v2i1.135

Keywords:

Artificial Intelligence (AI), Healthcare delivery, Saudi Arabia, Medical professionals, Education

Abstract

The integration of artificial intelligence (AI) into various sectors has garnered global attention, notable within the healthcare domain. In Saudi Arabia, discussions surrounding AI’s application in healthcare have been particularly pronounced, highlighted at significant gathering during the World Economic Forum in the nation’s capital. This paper aims to explore the multifaceted incorporation of AI into medical practices in Saudi Arabia, with a focus on enhancing healthcare delivery. Drawing upon insights from cultural anthropology and medicine, this study illuminates key aspects of AI adoption among Saudi medical professionals. Despite growing interest, there remains a dearth of comprehensive studies assessing AI acceptance, readiness, and proficiency among healthcare personnel, necessitating larger-scale investigations for more accurate insights. Current literature suggests that while some practitioners have embraced AI, many lack formal education and exhibit apprehension towards its utilization. Consequently, there is a pressing need for undergraduate and postgraduate educational programs tailored to AI integration within Saudi Arabia’s healthcare system. Such initiatives not only empower practitioners to harness AI’s full potential but also address concerns and apprehensions, particularly among senior professionals. By fostering a culture of AI education and proficiency, Saudi Arabia can effectively leverage AI to enhance healthcare outcomes and address emerging challenges in the medical landscape.

References

S. H. Bakry and B. A. A. Saud, “A Roadmap to AI: An Insight from the Saudi Vision 2030,” 2021, pp. 201–223.

G. Potesta, “Sustainable Development of Arabian Gulf Cities: Is Artificial Intelligence Conducive to Equitable Well-being for Users?,” Int. J. Soc. Sustain. Econ. Soc. Cult. Context, vol. 17, no. 2, pp. 99–113, 2021, doi: 10.18848/2325-1115/CGP/v17i02/99-113.

O. Hassan, “Artificial Intelligence, Neom and Saudi Arabia’s Economic Diversification from Oil and Gas,” Polit. Q., vol. 91, no. 1, pp. 222–227, Jan. 2020, doi: 10.1111/1467-923X.12794.

B. Karpatschof, “Artificial intelligence or artificial signification?,” J. Pragmat., vol. 6, no. 3–4, pp. 293–304, Aug. 1982, doi: 10.1016/0378-2166(82)90005-4.

D. E. Forsythe, Studying Those Who Study Us. Stanford University Press, 2002.

P. Poba-Nzaou, M. Galani, S. Uwizeyemungu, and A. Ceric, “The impacts of artificial intelligence (AI) on jobs: an industry perspective,” Strateg. HR Rev., vol. 20, no. 2, pp. 60–65, Jul. 2021, doi: 10.1108/SHR-01-2021-0003.

G. Rampersad, “Robot will take your job: Innovation for an era of artificial intelligence,” J. Bus. Res., vol. 116, pp. 68–74, Aug. 2020, doi: 10.1016/j.jbusres.2020.05.019.

R. Dias and A. Torkamani, “Artificial intelligence in clinical and genomic diagnostics,” Genome Med., vol. 11, no. 1, p. 70, Dec. 2019, doi: 10.1186/s13073-019-0689-8.

J. A. Nichols, H. W. Herbert Chan, and M. A. B. Baker, “Machine learning: applications of artificial intelligence to imaging and diagnosis,” Biophys. Rev., vol. 11, no. 1, pp. 111–118, Feb. 2019, doi: 10.1007/s12551-018-0449-9.

S. Shafi and A. V. Parwani, “Artificial intelligence in diagnostic pathology,” Diagn. Pathol., vol. 18, no. 1, p. 109, Oct. 2023, doi: 10.1186/s13000-023-01375-z.

R. Daneshjou, M. P. Smith, M. D. Sun, V. Rotemberg, and J. Zou, “Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms,” JAMA Dermatology, vol. 157, no. 11, p. 1362, Nov. 2021, doi: 10.1001/jamadermatol.2021.3129.

L. H. Nazer et al., “Bias in artificial intelligence algorithms and recommendations for mitigation,” PLOS Digit. Heal., vol. 2, no. 6, p. e0000278, Jun. 2023, doi: 10.1371/journal.pdig.0000278.

O. A. Osoba and W. W. IV, An Intelligence in Our Image. 2017.

M. Ali Alyousef et al., “Applying Artificial Intelligence in Clinical Laboratory: Clinical Laboratory Professionals’ Perception,” Int. J. Curr. Microbiol. Appl. Sci., vol. 12, no. 12, pp. 200–206, Dec. 2023, doi: 10.20546/ijcmas.2023.1212.023.

M. Ali Alyousef et al., “Impact f Aopplying Artificial Intelligence: Healthcare Professionals Insight (A Qualitative Survey Study In Hafr-Elbatin, Saudi Arabia),” Int. J. Adv. Res., vol. 10, no. 11, pp. 716–723, Nov. 2022, doi: 10.21474/IJAR01/15721.

K. Aboalshamat et al., “Medical and Dental Professionals Readiness for Artificial Intelligence for Saudi Arabia Vision 2030,” Int. J. Pharm. Res. Allied Sci., vol. 11, no. 4, pp. 52–59, 2022, doi: 10.51847/NU8y6Y6q1M.

T. Alanzi et al., “Barriers and Facilitators of Artificial Intelligence in Family Medicine: An Empirical Study With Physicians in Saudi Arabia,” Cureus, Nov. 2023, doi: 10.7759/cureus.49419.

R. Abdullah and B. Fakieh, “Health Care Employees’ Perceptions of the Use of Artificial Intelligence Applications: Survey Study,” J. Med. Internet Res., vol. 22, no. 5, p. e17620, May 2020, doi: 10.2196/17620.

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Published

2024-05-31