Satir Sayati, Muhammad
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Trends in Artificial Intelligence Research in Urology, Anesthesiology, Cardiology, and Otolaryngology: A Bibliometric Analysis (2019-2024) Sidharta Krisna, Muhammad; Alfi Reza, Muhammad; Ghiffary, Muamar; Satir Sayati, Muhammad; Awaluddin, Muhammad
International Journal of Health and Pharmaceutical (IJHP) Vol. 5 No. 1 (2025): February 2025 (Indonesia - Turkey - Malaysia - Australia - Iran)
Publisher : CV. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijhp.v5i1.403

Abstract

Background: The application of Artificial Intelligence (AI) in medicine is rapidly evolving, particularly in urology, anesthesiology, cardiology, and otolaryngology (ENT). AI is utilized in image-based diagnosis, patient monitoring, as well as the optimization of therapy and robotic surgery. However, research trends in AI within these fields have not yet been comprehensively mapped. Methods: This study employs bibliometric analysis to evaluate publication trends in AI across these four medical fields from 2019 to 2024. Data were collected from Google Scholar, PubMed, and Scopus, then analyzed using VOSviewer and R-Bibliometrix to identify the number of publications, keyword trends, institutional collaboration networks, and the most highly cited articles. Results: The number of AI-related publications in medicine has increased from 50 articles in 2019 to 130 articles in 2024. The United States, China, and the United Kingdom have the highest number of publications. Research trends indicate that deep learning and machine learning dominate, with broad applications in disease diagnostics and medical imaging. Cluster analysis reveals four main domains: anesthesiology (120 publications), cardiology (105 publications), urology (98 publications), and ENT (80 publications). Conclusion: AI has become an essential component in the advancement of modern medicine. With the increasing number of studies and multidisciplinary collaborations, AI is projected to continue expanding in data-driven diagnosis and therapy. However, challenges in clinical validation, regulation, and AI ethics must be addressed to ensure its safe and effective use.