AI integration in EHR systems in developing countries: a systematic literature review using the TCCM framework

Purpose This study aims to examine the adoption of artificial intelligence (AI) in electronic health record (EHR) systems in developing countries. It also aims to identify key challenges, explore the effectiveness of AI-driven solutions and propose a structured roadmap for optimizing AI integration to enhance health-care information management. Design/methodology/approach A systematic literature review (SLR) was conducted using the theory-context-characteristics-methodology (TCCM) framework to synthesize existing research on AI integration in EHR systems. The study analyzed peer-reviewed journal articles, conference proceedings and academic reports published between 2019 and 2024. The review applied a multi-stage synthesis process, categorizing studies based on theoretical perspectives, contextual barriers, AI technology characteristics and research methodologies. Findings The study identifies several barriers to AI integration in EHR systems in developing countries, including infrastructural limitations, interoperability challenges, workforce shortages, data fragmentation and regulatory gaps. Despite these challenges, AI-powered EHR solutions, such as machine learning, natural language processing (NLP), predictive analytics and computer vision, demonstrate potential for improving health-care workflows, reducing medical errors and enhancing real-time decision-making. The findings emphasize the need for regulatory frameworks, AI literacy programs, public-private partnerships (PPPs) and open-source AI models to support sustainable AI integration. Originality/value This study contributes to the literature by systematically reviewing AI integration in EHR systems within resource-limited settings, an area that remains underexplored. Unlike previous studies focused on AI implementation in high-income countries, this research provides a context-specific analysis tailored to developing countries. The study introduces a structured roadmap for AI-driven health-care transformation, offering policy recommendations and practical insights for health-care providers, AI developers and policymakers. The findings support digital health strategies aimed at enhancing patient outcomes and optimizing health-care information management in low-resource environments.