Evidence-Driven AI Governance for Healthcare: A PEARL-PATHWAY Analysis of Madinah

The rapid integration of artificial intelligence (AI) into healthcare systems has intensified the need for governance frameworks that ensure safety, accountability, ethical, and sustainable deployment. However, existing AI governance approaches are primarily articulated through high-level ethical and regulatory principles, with limited operational guidance tailored to specific healthcare contexts. This challenge is particularly evident in dynamic settings such as Al Madinah, Saudi Arabia, where demographic diversity, evolving healthcare needs, and large-scale public health pressures, including the presence of millions of visitors annually during Hajj and Umrah, require adaptive and context-aware governance. This study presents an evidence-driven approach to AI governance analysis that directly links empirical healthcare needs with regulatory frameworks. It integrates the PEARL framework to systematically analyse an initial corpus of 4,277 healthcare publications related to Madinah, refined to 243 articles through inclusion and exclusion criteria, extracting structured representations of healthcare priorities, with the PATHWAY framework to evaluate alignment between these needs and both Saudi Arabian and international AI governance frameworks. This enables a systematic assessment of governance applicability, identification of gaps, and analysis of associated risks. The results reveal that while existing frameworks provide strong foundations in terms of privacy, ethics, and risk-based regulation, they lack operational pathways tailored to domain-specific healthcare requirements and local contexts. Key gaps are identified in areas including epidemiological surveillance, behavioural health, maternal and paediatric care, environmental health integration, and generative AI in public health communication. By bridging empirical evidence with governance analysis, this study advances a structured approach to domain-informed and context-sensitive AI governance. It contributes to the emerging field of computational policy analysis and provides evidence-driven insights for developing adaptive, scalable, and trustworthy AI governance strategies in healthcare systems.