Saudi Arabia’s healthcare AI rollout stalls amid policy gaps and skills shortage

Despite the promise, the researchers noted that AI adoption remains scattered and experimental, with a lack of centralized policy steering its deployment. Use cases are mostly confined to individual hospital initiatives or private sector pilots. The study highlights that implementation is uneven across public and private sectors, with most technological innovation concentrated in urban centers and higher-tier medical facilities.

A new systematic review reveals that Saudi Arabia’s integration of artificial intelligence (AI) into its healthcare system remains underdeveloped, constrained by infrastructural limitations, a lack of regulatory frameworks, insufficient investment, and poor technological literacy among health professionals.

Published in Frontiers in Artificial Intelligence under the title “The Current State, Challenges, and Future Directions of Artificial Intelligence in Healthcare in Saudi Arabia: Systematic Review”, the study evaluates AI’s adoption trajectory in the Kingdom, providing critical insights into the readiness, roadblocks, and potential pathways for transformation under Vision 2030.

How is AI currently being used in Saudi Arabia’s healthcare system?

The study examined 13 peer-reviewed articles published between 2018 and 2023, identifying several pilot initiatives and emerging areas of implementation. AI is primarily used in diagnostics, radiology, clinical decision support systems (CDSS), telehealth, and predictive analytics. During the COVID-19 pandemic, AI-assisted remote monitoring and telemedicine platforms were adopted to enhance accessibility, especially in emergency response scenarios. These tools were supported by the deployment of machine learning and deep learning algorithms capable of processing large datasets and assisting with disease pattern recognition and prognosis modeling.

Despite the promise, the researchers noted that AI adoption remains scattered and experimental, with a lack of centralized policy steering its deployment. Use cases are mostly confined to individual hospital initiatives or private sector pilots. The study highlights that implementation is uneven across public and private sectors, with most technological innovation concentrated in urban centers and higher-tier medical facilities.

Educational institutions have also begun integrating AI into healthcare training programs, but the reach and depth of these efforts are inconsistent. While the National Transformation Program under Vision 2030 aims to overhaul healthcare delivery through digitization, the review finds that the sector still lacks the infrastructure and institutional capacity to scale AI effectively across the system.

What are the main obstacles to effective AI integration?

According to the review, Saudi Arabia’s most pressing barriers to AI integration include the absence of comprehensive regulatory frameworks, inadequate investment in infrastructure and training, and cultural resistance to technological disruption. Many healthcare professionals remain unprepared to operate AI tools, citing limited technical training and discomfort with automated decision-making. Additionally, the study notes a significant skills gap within the existing medical workforce that affects both the deployment and maintenance of AI systems.

The lack of standardization and national governance around AI in healthcare presents a further challenge. Without ethical guidelines and legal standards, institutions are hesitant to adopt AI-driven solutions at scale due to concerns over accountability, patient data privacy, and compliance. These concerns are exacerbated by cybersecurity risks, especially given Saudi Arabia’s strict data governance environment, which some respondents in the reviewed literature identified as a deterrent to investor confidence.

On the patient side, mistrust of automated systems and limited exposure to AI technologies hinder broader acceptance. The study also points to fragmented efforts in AI education and research, noting that universities and public health institutions need stronger coordination to support an AI-ready ecosystem. Many healthcare settings, particularly in less-developed regions, lack even the basic digital infrastructure needed to support AI platforms.

Even in sectors where AI shows maturity, such as radiology, the study finds that usage is still narrow and largely driven by a few well-resourced institutions. A lack of cross-institutional data sharing, underdeveloped algorithm validation procedures, and unclear liability frameworks all combine to suppress innovation and slow AI’s path from pilot to policy.

What future strategies are recommended for advancing AI in Saudi healthcare?

The review outlines a multipronged strategy for Saudi Arabia to align its healthcare system with Vision 2030’s digitization and innovation goals. Central to this approach is the development of robust policy frameworks and regulatory mechanisms to guide the ethical and safe integration of AI. These would include guidelines for data protection, algorithm transparency, patient consent, and legal liability, currently all underdeveloped or absent in the existing healthcare governance structure.

Investment in infrastructure and human capital is another top priority. The authors argue that a nationwide push to train healthcare professionals in AI literacy is critical for long-term sustainability. Targeted training programs, user-friendly AI interfaces, and dedicated professional development pathways must be rolled out in both public and private health sectors.

The study also calls for the establishment of national AI research centers focused on healthcare applications. These hubs would facilitate coordinated research, promote data sharing across institutions, and accelerate clinical validation of AI models. Importantly, researchers emphasize the need for policy alignment between education, healthcare, and technology ministries to foster cross-sector collaboration and build a shared AI development roadmap.

Public trust must also be addressed through outreach and awareness campaigns that demystify AI tools, explain their benefits, and clarify their limitations. These efforts are essential to reduce patient resistance and support uptake, particularly for AI-driven platforms used in primary care or mental health services where human interaction is often paramount.

Lastly, the review recommends increased focus on context-specific AI applications such as chronic disease management, rural health delivery, and health system optimization, which offer high impact potential in the Saudi context. Pilot programs in these areas should be expanded and evaluated rigorously to inform scalable models of success.