THE ROLE OF ARTIFICIAL INTELIGENCE IN ELECTRONIC MEDICAL RECORDS FOR PATIENT-CENTERED CARE: A LITERATURE REVIEW

Abstract

The incorporation of artificial intelligence (AI) within electronic medical records (EMRs) has the potential to transform these systems from passive data repositories into intelligent, patient-oriented platforms (Min et al., 2024; Ali, 2023). This literature review consolidates research published between 2015 and 2025 concerning AI-EMR integration, emphasizing four primary domains: (1) natural language processing and data enrichment (Biswas & Talukdar, 2024); (2) predictive analytics and clinical decision-making support (Anakal & Soumya, 2024; Prentzas et al., 2023); (3) automation of documentation and workflow optimization (Min et al., 2024); and (4) governance, ethical considerations, and challenges to adoption (Chen et al., 2023; Solaiman & Cohen, 2024). Findings indicate that AI can enhance patient-centered care by expanding data quality, reducing the documentation load on clinicians, facilitating proactive insights, and enabling collaborative decision-making. These advantages, however, depend on effectively managing issues such as bias, transparency, reliability, workflow compatibility, and legal responsibility (Prentzas et al., 2023; Chen et al., 2023). The paper presents a conceptual overview of AI functionalities in EMRs and outlines recommendations for design, assessment, and governance strategies to ensure alignment with patient-centered care values.