Addressing Healthcare Disparities with Computer Vision and AI: Investigating the Potential of Intelligent Systems to Improve Access, Equity, and Outcomes in Underserved Populations

Abstract

Healthcare disparities, characterized by unequal access to healthcare services and disproportionate health outcomes among different populations, remain a significant challenge in the global healthcare landscape. Underserved populations, including low-income communities, racial and ethnic minorities, and rural residents, often face barriers to receiving timely and quality healthcare. Computer vision and artificial intelligence (AI) technologies have the potential to address these disparities by developing intelligent systems that improve access, equity, and outcomes in underserved populations. This research article explores the role of computer vision and AI in addressing healthcare disparities, focusing on their applications in telemedicine, mobile health, and community-based interventions. By examining case studies, current research, and future prospects, we aim to highlight the transformative potential of these technologies in bridging the healthcare gap and promoting health equity. The article also discusses the challenges and ethical considerations associated with the implementation of AI-driven systems in underserved communities, emphasizing the importance of inclusive design, cultural competence, and community engagement.