Artificial Intelligence In Saudi Healthcare: A Systematic Review Of Clinical Applications Supporting Vision 2030 Goals

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare systems globally. In Saudi Arabia, its integration is central to Vision 2030, which aims to modernize healthcare services, enhance efficiency, and promote innovation. This systematic review evaluates the clinical applications of AI in Saudi healthcare, focusing on effectiveness, implementation challenges, and alignment with national goals. Twenty-two peer-reviewed studies published between January 2018 and March 2024 were analyzed. Sourced from PubMed, Scopus, and Web of Science, these studies addressed AI use in diagnostics, medical imaging, clinical decision support, and operational improvement. PRISMA guidelines were applied to ensure methodological rigor. Data were extracted on clinical outcomes, AI model types, and barriers to adoption. AI tools demonstrated strong diagnostic performance, with sensitivity and specificity ranging from 82% to 97%. Predictive models were effective in forecasting COVID-19 outcomes, ICU readmissions, and chronic disease risks. Deep learning applications in radiology outperformed conventional methods in accuracy and speed. However, challenges such as limited data interoperability, unclear regulations, and insufficient clinician training hinder broader adoption. The Saudi Data and Artificial Intelligence Authority (SDAIA) plays a key role in advancing AI through national strategies and cross-sector collaboration. Despite progress, full realization of AI’s potential requires investment in digital infrastructure, ethical governance, and workforce development. In conclusion, AI is reshaping Saudi healthcare and aligns with Vision 2030’s goals. Strategic implementation and inclusive policy frameworks are essential to ensure scalable, equitable, and clinically validated AI integration across the healthcare system.

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