Mapping artificial intelligence applications in electronic medical records research

Abstract

The integration of electronic medical records (EMR) and artificial intelligence (AI) in healthcare improves data accessibility, security, diagnostic accuracy, personalized care, and overall system performance. Despite increasing interest, a comprehensive understanding of the field’s development, key contributions, and dominant research themes remains limited. This study presents a bibliometric analysis of 681 articles selected from 1893 initial records retrieved from the Scopus database (2015–2025) using the keywords “electronic medical record” AND “artificial intelligence.” Data were analyzed using Microsoft Excel for trend analysis and VOSviewer for keyword co-occurrence and thematic clustering. Results show steady publication growth, mainly from developed countries and health informatics institutions. Four main research themes emerged: i) AI adoption in healthcare systems, ii) patient characteristics and clinical assessment, iii) predictive models and machine learning (ML) algorithms, and iv) deep learning (DL) and diagnostic accuracy. Nevertheless, research gaps persist in areas such as patient safety, data privacy, ethical issues, primary care implementation, healthcare workforce roles, and specific algorithmic approaches. Trust in AI systems also requires deeper investigation.