The authors review how artificial intelligence can help address health inequalities in primary care, examining its potential to improve access, diagnostic accuracy, and personalized care in underserved populations. The paper explores several AI-driven technologies—including telemedicine platforms, clinical decision-support systems, and data-driven risk stratification tools—that can support earlier detection of disease, enhance diagnostic capabilities, and enable more tailored interventions. By integrating multiple sources of patient data and identifying patterns that may not be easily recognized through traditional clinical approaches, AI systems have the potential to support clinicians in delivering more consistent and evidence-based care, particularly in resource-constrained environments. The review highlights how these technologies may improve healthcare delivery in primary care settings by expanding access to services, supporting remote monitoring, and helping clinicians manage large patient populations more effectively. 

From a public-health perspective, the implications of these technologies are significant. AI-enabled tools could help reduce disparities in healthcare by improving early diagnosis, facilitating preventive care, and supporting more equitable distribution of clinical resources across populations. However, the authors emphasize that these benefits depend heavily on how AI systems are designed and implemented. Challenges such as algorithmic bias, unequal access to digital infrastructure, privacy concerns, and lack of representation in training data could potentially reinforce existing inequalities if not carefully addressed. The review therefore stresses the importance of equity-centered development, transparent evaluation, and strong governance frameworks to ensure that AI systems genuinely contribute to reducing health disparities rather than widening them. When implemented responsibly, the authors suggest that AI could become a powerful tool for strengthening primary care systems and improving health outcomes at the population level.