TITLE:
Artificial Intelligence in Primary Care: Opportunities and Challenges in the Canadian and American Healthcare Systems
AUTHORS:
Ifeoluwa Claudius Daramola, Onyinyechukwu C. Ezegwui, Frederick Kofi Ametepe, Lotechukwu Austin Ezeilo, Joy Adewale Felix, Joy Adurapemi Odedele, Sefiyah Lawal, Oghenefejiro Deborah Ebresafe
KEYWORDS:
Artificial Intelligence (AI), Primary Care, Canada, United States, Clinical Decision Support Systems (CDSS), Predictive Analytics, Natural Language Processing (NLP), Telehealth, Remote Patient Monitoring (RPM), Health System Efficiency, Algorithmic Bias
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.10,
October
31,
2025
ABSTRACT: Background: Artificial intelligence (AI) technologies, including machine learning, natural language processing, and decision-support systems, are increasingly explored in primary care to improve diagnostic accuracy, reduce clinician workload, and enhance patient access. While specialty care has seen more rapid adoption, primary care in Canada and the United States presents unique challenges and opportunities for AI integration due to structural, regulatory, and operational differences. Methods: This systematic review followed PRISMA guidelines and applied the PICO framework. A comprehensive search of PubMed, Scopus, Google Scholar, CINAHL, IEEE Xplore, and the Cochrane Library identified studies published between 2010 and 2025. Inclusion criteria encompassed peer-reviewed empirical studies, reviews, and policy reports addressing AI applications in primary care in Canada and/or the United States. Out of 4362 articles screened, 25 met the eligibility criteria and were included in the final synthesis. Results: AI applications in primary care were clustered into five domains: diagnostic support, risk prediction, documentation and administrative efficiency, patient engagement, and system optimization. Evidence demonstrated improvements in diagnostic accuracy, chronic disease management, documentation burden reduction, and remote patient monitoring. However, challenges included fragmented data systems, algorithmic bias, limited clinician trust, workflow disruption, regulatory uncertainty, cost barriers, and variable public acceptance. The review highlighted that clinician engagement, transparent AI design, and robust data governance were critical facilitators of adoption. Conclusion: AI has significant potential to enhance the efficiency, accessibility, and personalization of primary care in Canada and the United States. However, widespread adoption remains constrained by systemic, ethical, and infrastructural barriers. Successful integration will depend on co-design with clinicians and patients, regulatory reform, equity-focused AI development, and sustainable funding models. AI’s future role in primary care will be shaped less by technological capacity and more by governance, trust, and alignment with the human-centered values of frontline healthcare.