Deploying AI for global health equity: A readiness matrix and case studies from low- and middle-income countries

Artificial intelligence (AI) is transforming healthcare and can reduce inequalities through optimized resource allocation, timely diagnosis, and personalized care. However, over 3.5 billion people still lack access to basic health services, primarily in low- and middle-income countries (LMICs) due to infrastructural, socioeconomic, and cultural barriers. This article examines the opportunities and limitations of AI in advancing global health equity, focusing on region-specific challenges. It presents a narrative review and case-based synthesis of AI deployment in LMICs, using examples from India, Peru, Senegal, Kenya and Cambodia. Key implementation factors include infrastructure, workforce capacity, digital literacy, data availability, resource constraints, and cultural fit. A tiered AI readiness matrix is introduced to compare preparedness and support policymakers in prioritizing AI applications. Ethical risks such as algorithmic bias, data sovereignty and transparency are also emphasized. Successful AI deployment requires localized adaptation, participatory design, and supportive governance. This study provides a strategic framework for policymakers, developers, and global health actors who aim to implement AI equitably and effectively.