AI and Blockchain for Secure Healthcare Data Management: A Bibliometric Analysis of Research Trends and Thematic Clusters (2020–2025)

Abstract: The convergence of artificial intelligence (AI) and blockchain has become an active axis of interdisciplinary research in healthcare data security. This paper reports a bibliometric analysis of 434 Scopus-indexed articles published between 2020 and 2025, with data collection and processing performed on 20 April 2026. The objective is to map the intellectual structure, collaborative dynamics, and thematic composition of this expanding field. The corpus was analyzed using the bibliometrix R package (version 4.3.0) and VOSviewer (version 1.6.20). The increasing research output is evidenced by an annual growth rate of 34.8%, with publication volume growing from 10 articles in 2020 to a maximum of 152 articles in 2025 across a total of 198 unique venues. Keyword co-occurrence analysis, processed through the Louvain community detection algorithm on 250 high-frequency terms with association-strength normalization, produced four thematic clusters: Blockchain and Privacy-Preserving Techniques, Healthcare Systems and Cybersecurity Infrastructure, Artificial Intelligence and Clinical Diagnostics, and Electronic Health Records and Interoperability. India, China, Saudi Arabia, and the United States lead scholarly output. The international co-authorship rate of 49.31% reflects the globally distributed nature of the research community. IEEE Access and the IEEE Journal of Biomedical and Health Informatics are the dominant publication venues. Federated learning occupies a structurally central position, with a betweenness centrality of 101.5, acting as the principal methodological bridge between the two technologies. An average of 27.96 citations per document confirms the above-average scholarly impact of the corpus. The results provide researchers, practitioners, and policymakers with an evidence-based map of the field’s trajectory, its most productive research directions, and its remaining structural gaps. The underlying dataset, search logs, and analysis scripts are released openly to support full reproducibility.

Maroufi Mohammed, Lamzabi Siham and Ziti Soumia. “AI and Blockchain for Secure Healthcare Data Management: A Bibliometric Analysis of Research Trends and Thematic Clusters (2020–2025)”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.4 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170452

@article{Mohammed2026,
title = {AI and Blockchain for Secure Healthcare Data Management: A Bibliometric Analysis of Research Trends and Thematic Clusters (2020–2025)},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170452},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170452},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {4},
author = {Maroufi Mohammed and Lamzabi Siham and Ziti Soumia}
}