
A systematic review of the role of artificial intelligence in health equity: telemedicine for underserved areas
Mohammad Mohseni 1 ©, Shakila Shahram 2 ℗
Abstract
Introduction: Establishing health equity in underserved areas remains a fundamental challenge for global healthcare systems. In this context, emerging technologies such as artificial intelligence (AI) and telemedicine have emerged as promising solutions to achieve equitable healthcare. This article examines existing evidence to analyze the role of these technologies in reducing health disparities and improving access to healthcare services in underserved regions, while comprehensively evaluating research findings, challenges, and future opportunities in this field. This study aimed to demonstrate that AI and telemedicine technologies show significant potential in reducing health inequalities in underserved regions, with documented successes in improving access to specialized services, increasing disease detection rates, and reducing healthcare costs. Search Strategy: This systematic review was conducted in 2025 based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach. Primary studies were identified through searches of PubMed, Scopus, Embase, Web of Science, and ProQuest databases for publications between 2015-2025. Search terms included Artificial intelligence, Health equity, Telemedicine, Underserved areas, and related terms such as Machine learning, Deep learning, AI in healthcare, Virtual medicine, mHealth, eHealth, and Telehealth, using Boolean operators (And/Or). Results: The systematic review analyzed applications of AI in medical services, health equity considerations, and telemedicine implementations in underserved areas. After database searches with appropriate keywords, 26 articles meeting inclusion criteria were analyzed. Most studies originated from the United States (16 articles) and China (4 articles). Research methodologies included experimental (10 articles), qualitative (6 articles), and descriptive-analytical (3 articles) approaches, along with retrospective cohort studies, clinical trials, cross-sectional studies, case studies, and quantitative methods. Conclusion and Discussion: The Findings showed that artificial intelligence and telemedicine technologies have significant potential in reducing health inequalities in underserved regions; However, key challenges were identified, including algorithmic biases, technical infrastructure deficiencies, and cultural resistance to new technologies. The study concludes that AI has transformative potential for achieving health equity, though its success depends on incorporating demographic diversity, local needs, and ethical principles into technology design. The integration of telemedicine with AI could serve as a key strategy for attaining Universal Health Coverage (UHC) in underserved areas, provided that implementation and structural challenges are addressed through interdisciplinary collaboration.
Keywords: Artificial intelligence, Health equity, Systematic review, Telemedicine, Underserved areas