Systematic Review and Bibliometric Mapping on Image Processing in Electronic Health Records

Abstrak

The integration of medical image processing techniques into Electronic Health Records (EHR) has become a vital element in the transformation of modern digital healthcare services. This study aims to conduct a Systematic Literature Review (SLR) and bibliometric analysis to evaluate current research trends, patterns, and gaps from 2021 to 2025. Using the PRISMA framework and data from the Scopus database, this study analyzes 23 selected articles visualized using VOSviewer software. The results reveal a significant surge in publications, driven by the adoption of Artificial Intelligence (AI) and Deep Learning, which have been proven to improve diagnostic accuracy and facilitate early detection of critical diseases. Although these technologies support better clinical decision-making, major challenges related to system interoperability, data standardization, and patient privacy security remain substantial obstacles that need to be overcome. The study also highlights the role of emerging technologies such as the Internet of Medical Things (IoMT) and blockchain as potential solutions for data security. In conclusion, this research provides strategic guidance for developers and policymakers to create a more interoperable, secure, and efficient EHR ecosystem.