Toward Equitable AI Deployment: Overcoming Barriers to Breast Cancer Diagnosis in Rural and Underserved Communities

Early access to breast cancer diagnosis is still a crucial issue in rural and underprivileged areas, where medical infrastructure, specialist presence, and awareness are limited. Although AI models have proved highly accurate in detecting breast cancer utilizing mammography and clinical data, the successful deployment of these tools in low-resource areas poses significant challenges. This paper discusses the most significant hurdles in the adoption of AI-based breast cancer diagnostic systems in rural settings, such as infrastructural gaps in technology, financial limitations, shortages of trained staff, and patient-clinician trust issues. Based on the existing literature, case studies, and public health paradigms, this research also delineates probable strategies to overcome these hindrances, e.g., combining AI with mobile health platforms, IoT-based diagnostic platforms, community health worker training initiatives, and policy-level initiatives to subsidize technology uptake. Through its focus on deployment concerns related to technical performance, the research highlights the avenues that must be pursued to ensure fair access to life-saving AI technology in breast cancer diagnosis and screening.