Journals / Journal of Health Sciences and Medicine / 2026 / Volume: 9 Issue: 5

Bibliometric analysis of emergency medicine specialization theses on Artificial Intelligence in Turkiye (2020-2025)

Esat Barut(HARRAN ÜNİVERSİTESİ, TIP FAKÜLTESİ, DAHİLİ TIP BİLİMLERİ BÖLÜMÜ, ACİL TIP ANABİLİM DALI)0000-0003-0844-0468
Kemal Barut(SAĞLIK BİLİMLERİ ÜNİVERSİTESİ, DİYARBAKIR GAZİ YAŞARGİL SAĞLIK UYGULAMA VE ARAŞTIRMA MERKEZİ)0000-0001-8758-726X
Pages
—
Publication Date
2026-09-19
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Future strategies should diversify research topics and encourage the conversion of theses into publications.

📌 3 Key Takeaways

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Objective & Hypothesis:Aims: The aim of this study is to examine the bibliometric characteristics and scientific production trends of emergency medicine specialization theses on Artificial Intelligence (AI) in Turkiye.
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Key Findings & Data:Methods: In our descriptive bibliometric analysis, emergency medicine specialization theses prepared between 2020-2025 were scanned in the YÖK National Thesis Center database.
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Conclusion & Impact:Future strategies should diversify research topics and encourage the conversion of theses into publications.
🔬Design:Review & Systematic Survey
📍Scope:2020-2025 Verileri
⚙️Variables:Emergency medicine · bibliometric analysis · medical specialization thesis

Abstract

Aims: The aim of this study is to examine the bibliometric characteristics and scientific production trends of emergency medicine specialization theses on Artificial Intelligence (AI) in Turkiye. Methods: In our descriptive bibliometric analysis, emergency medicine specialization theses prepared between 2020-2025 were scanned in the YÖK National Thesis Center database. Using AI-related keywords, 31 theses meeting inclusion criteria were included. Statistical analyses were performed using SPSS; in addition to descriptive statistics, linear regression analysis, Mann-Kendall trend test, compound annual growth rate (CAGR) calculation, the Fisher-Freeman-Halton exact test, Shannon Entropy, Pielou Evenness Index, and Herfindahl-Hirschman Index were utilized. Results: All theses were completed between 2022-2025; none were encountered in 2020 or 2021. An increasing trend was observed in thesis numbers, with a CAGR of 104.1%. Theses were mostly produced in Ankara (35.5%) and İstanbul (16.1%), with university hospitals (67.7%) being predominant. Male authors (67.7%) were in the majority. Professors supervised 45.2% of theses. Radiology/imaging (41.9%) and trauma/orthopedics (32.3%) were the most studied topics, with increasing interest in new areas like triage and large language models, showing diversification over time. Additionally, as of the evaluation date, no thesis was published in a national or international peer-reviewed journal. Conclusion: The number of AI specialization theses in emergency medicine in Turkiye is increasing significantly. Research generally concentrates on areas compatible with daily emergency practice. The absence of publication conversion should be interpreted cautiously, as it may reflect both the short and heterogeneous follow-up periods of the theses and potential difficulties in academic literature integration. Future strategies should diversify research topics and encourage the conversion of theses into publications.

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Cite This Article

Barut, E., Barut, K. (2026). Bibliometric analysis of emergency medicine specialization theses on Artificial Intelligence in Turkiye (2020-2025). Journal of Health Sciences and Medicine, Volume: 9 Issue: 5. https://doi.org/10.32322/jhsm.1964910

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