Integrating Artificial Intelligence (AI) in Primary Health Care (PHC) Systems: A Framework-Guided Comparative...

Background/Objectives: The integration of artificial intelligence (AI) into primary health care (PHC) holds significant potential to enhance efficiency, equity, and clinical decision-making. However, its implementation remains uneven across contexts. This study aimed to identify the systemic, contextual, and governance-related determinants influencing AI readiness in PHC, comparing two distinct health systems, Quebec (Canada) and Iran. Methods: A qualitative, comparative design was employed. Data were collected through semi-structured interviews and focus group discussions with key informants in both settings. A framework-guided content analysis was conducted based on the four Primary Care Evaluation Tool (PCET): stewardship, financing, resource generation, and service delivery. The analysis explored shared context-specific challenges and requirements for AI implementation in PHC. Results: Analysis revealed that AI readiness is shaped more by systemic coherence rather than technological availability alone. Across both contexts, governance- and financing-related challenges were reported by the majority of participants, alongside limited data interoperability. In Quebec, challenges were more commonly articulated around operational and ethical concerns, including workflow integration, transparency, and professional trust. In contrast, participants in Iran emphasized foundational deficiencies in governance stability, financing mechanisms, and digital infrastructure as primary barriers. Across both settings, adaptive governance, sustainable investment, data standardization, and workforce capacity-building consistently emerged as key requirements for AI integration in PHC. Conclusions: AI readiness in PHC is a multidimensional process, in which implementation priorities must align with system maturity. This comparative analysis underscores that while high-resource systems must prioritize ethical integration and workflow alignment, middle-resource settings require foundational investments in governance and infrastructure. This reinforces that AI readiness is a context-dependent and phased process rather than a one-size-fits-all endeavor.