Digital Divides and AI in Medicine: A Socio-Technological Analysis of Access and Inclusion

Abstract

Artificial Intelligence (AI) is revolutionizing the field of medicine, offering transformative potential for diagnosis, treatment, and healthcare delivery. However, the integration of AI also exposes and exacerbates existing digital divides, creating disparities in access and inclusion. This paper presents a socio-technological analysis of how digital inequality influences the adoption and outcomes of AI in medicine. It explores critical dimensions such as socioeconomic status, geographic location, infrastructure availability, digital literacy, and algorithmic bias. Marginalized populations often lack access to the technological infrastructure necessary to benefit from AI-driven health tools, including telemedicine platforms, diagnostic algorithms, and predictive analytics. Furthermore, the data used to train medical AI systems frequently underrepresent minority and low-income groups, leading to systemic biases and decreased accuracy in these populations. The paper also examines institutional and policy-level challenges, emphasizing the need for inclusive data governance, equitable technology deployment, and culturally competent AI design. Drawing on global case studies, this analysis underscores the importance of a multi-stakeholder approach that integrates ethical considerations, community participation, and public health frameworks. By addressing the socio-technological barriers to AI in medicine, the study advocates for strategies that ensure equitable access and avoid the risk of deepening health disparities in the digital age. The findings highlight that the promise of AI in healthcare can only be realized through intentional efforts to bridge digital divides and foster inclusive innovation.