Assessing Primary Care Physicians’ Readiness for AI-Based Adaptive Learning: Perceptions, Barriers, and Learning Needs in Northern Saudi Arabia

Background and objectives: Artificial intelligence (AI)-based adaptive learning has the potential to strengthen clinical decision-making and enhance quality of care at primary health centers. The present study assessed the perceptions, barriers, and learning needs involved in AI-based adaptive learning among primary care physicians in Northern Saudi Arabia. Methods: We used a cross-sectional study design to obtain data from 285 primary care physicians of different cadres working in various primary health centers. A validated data collection tool was used to measure three domains: perceptions, barriers, and learning needs. A multivariable analysis was carried out to identify the factors associated with these three domains. Results: Among the studied participants, low perceptions were observed in 55.1% of physicians; they were higher among those aged >40 years (p = 0.019) and non-Saudi nationals (p = 0.003). High barriers were reported by 42.5% of respondents, and this was higher among those aged >40 years (p = 0.031). Learning needs were higher among non-Saudi nationals (p = 0.017) and those with >10 years of experience (p = 0.007). The perception and learning need scores were positively correlated, and barrier scores were negatively correlated with the other two domains (p < 0.001). Conclusions: The authorities concerned may consider implementing targeted measures for AI-based adaptive learning. Moreover, efforts should be made to reduce the barriers to AI-based adaptive learning at all levels. These measures could strengthen primary care practice and enhance patient care.