Authors : Hazel Galas Lampitoc; Dr. Reagan Recafort

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/3bj7cr5k

Scribd : https://tinyurl.com/5cbh7jx2

DOI : https://doi.org/10.38124/ijisrt/26feb552

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : With greatly improved diagnostic accuracy and the ability to make patient-centered clinical decisions, artificial intelligence (AI) is at the heart of the revolutionary progress in imaging. With Saudi Vision 2030 digital healthcare strategy, AI imaging techniques have emerged as a major advancement. But a significant portion of healthcare organizations are plagued by IT governance challenges, interoperability issues, cybersecurity risks and workforce development — obstacles — that can delay the adoption of AI solutions. That is why this qualitative research has created a strategic IT management framework guiding healthcare organisation towards practical sustainability with AI-augmented biomedical imaging. Based on academic literature, state health policy initiatives, and experience with the healthcare setting in Saudi Arabia. Such conclusions are congruent with the pressing need for a paradigm shift in healthcare system design, data governance and digital infrastructure, and ongoing education for health care workers. The framework described here is designed to help health care practitioners develop safe and scalable AI-driven imaging tools consistent with the Saudi Vision as part of a strategy of healthcare transformation.

Keywords : Artificial Intelligence, Biomedical Imaging, IT Management, Healthcare Systems, Saudi Arabia, Digital Transformation, Vision 2030, Interoperability and Cybersecurity. Introduction.

References :

1. EBS Journal, “Optimizing Healthcare Technology Management (HTM): AI‑Driven Predictive Maintenance (PdM) for Medical Imaging Systems,” EBS Journal, 2024.
2. A. Ebadi and A. —, “AI‑Powered Biomedical Imaging: Recent Achievements and Challenges,” Current Opinion in Biomedical Engineering, 2026.
3. Optum, “The Role of AI in Enterprise Imaging,” Optum Insights, 2024.
4. Journal of Medical Imaging and Radiation Sciences, “Current and Potential Applications of Artificial Intelligence in Medical Imaging Practice: A Narrative Review,” JMIRS, vol. 54, no. 2, pp. 376–385, 2023.
5. W. He, X. Wu, S. Zhang, B. Zhang, Z. Jin, J. Sun, and X. Jiang, “Generative Artificial Intelligence in Medical Imaging: Current Landscape, Challenges, and Future Directions,” INMD, 2025.
6. S. Lundervold and A. Lundervold, “An Overview of Deep Learning in Medical Imaging Focusing on MRI,” Z Med Phys, vol. 29, no. 2, pp. 102–127, 2019.
7. G. Litjens et al., “A Survey on Deep Learning in Medical Image Analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017.
8. Saudi Data and AI Authority (SDAIA), “National Strategy for Data and Artificial Intelligence,” Government of Saudi Arabia, 2023.
9. Ministry of Health (Saudi Arabia), “Digital Health Transformation Strategy,” MOH Publications, 2022.
10. Health Holding Company (HHC), “AI‑Enabled Clinical Services Deployment Report,” Riyadh, 2024.
11. World Health Organization, “Ethics and Governance of Artificial Intelligence for Health,” WHO Guidance Document, 2021.
12. R. M. Summers, “Artificial Intelligence and Radiology: Opportunities, Challenges, and Strategies,” Radiology, vol. 295, no. 3, pp. 475–478, 2020.
13. T. Davenport and R. Kalakota, “The Potential for Artificial Intelligence in Healthcare,” Future Healthcare Journal, vol. 6, no. 2, pp. 94–98, 2019.
14. H. J. Kim et al., “Interoperability Challenges in Healthcare IT Systems,” Healthcare Informatics Research, vol. 26, no. 1, pp. 3–12, 2020.
15. N. Sultan, “Cloud Computing for AI‑Driven Healthcare: Opportunities and Risks,” International Journal of Information Management, vol. 49, pp. 242–250, 2019.
16. M. Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, Basic Books, 2019.
17. A. Esteva et al., “A Guide to Deep Learning in Healthcare,” Nature Medicine, vol. 25, pp. 24–29, 2019.
18. K. Reddy and S. Gupta, “Cybersecurity in AI‑Enabled Healthcare Systems,” Health Informatics Journal, vol. 27, no. 4, pp. 1–15, 2021.
19. A. Al‑Khalifa, “Digital Transformation in Saudi Arabia’s Healthcare Sector: Progress and Challenges,” Saudi Journal of Health Systems Research, vol. 2, no. 1, pp. 15–28, 2023.
20. H. Al‑Mansour, “AI Readiness in the Gulf Healthcare Sector,” Middle East Journal of Digital Health, vol. 1, no. 2, pp. 45–59, 2024.