Research trends in Dermatology specialty theses: A bibliometric analysis of the national thesis center in Turkey (2020–2025)

Authors

DOI:

https://doi.org/10.5281/zenodo.18895284

Keywords:

Dermatology specialty theses, Psoriasis bibliometric analysis, Research trends, Inflammatory dermatoses

Abstract

Objective: Dermatology specialty theses reflect evolving research priorities and scientific focus areas within the discipline. Evaluating their thematic distribution provides insight into academic trends and emerging research directions. To analyze dermatology specialty theses completed between 2020 and 2025 in terms of annual output and disease-oriented research focus.

Materials and methods: A retrospective bibliometric analysis was conducted using data obtained from the National Thesis Center database. A total of 529 dermatology specialty theses were evaluated. Theses were categorized according to primary disease focus, and the ten most frequently studied disease groups were identified. Annual proportional distributions were calculated using descriptive statistics.

Results: The number of theses increased throughout the study period, peaking in 2025. Psoriasis was the most frequently studied disease overall (20.2%), followed by acne vulgaris and skin malignancies. In recent years, a relative increase was observed in hidradenitis suppurativa and bullous diseases. Subgroup analysis demonstrated that inflammatory markers, systemic associations, and immunologic parameters were among the most commonly investigated research themes.

Conclusion: Between 2020 and 2025, dermatology specialty research demonstrated sustained interest in highly prevalent inflammatory dermatoses while increasingly focusing on systemic involvement and immunologic mechanisms. These findings reflect alignment between specialty training research and global dermatology trends.

References

Griffiths CEM, Armstrong AW, Gudjonsson JE, Barker JNWN. Psoriasis. Lancet. 2021;397(10281):1301-15.

Dalgard FJ, Gieler U, Tomas-Aragones L, Lien L, Poot F, Jemec GBE, et al. The psychological burden of skin diseases: a cross-sectional multicenter study among dermatological out-patients in 13 European countries. J Invest Dermatol. 2015;135(4):984-91.

Leiter U, Eigentler T, Garbe C. Epidemiology of skin cancer. Adv Exp Med Biol. 2014;810:120-40.

Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res. 2021;133:285-96.

Tang ZJ, Yang JR, Yu CL, Dong MH, Wang R, Li CX. A Bibliometric Analysis of Global Research Trends in Psoriasis and Metabolic Syndrome. Clin Cosmet Investig Dermatol. 2024;17:365-82.

Armstrong AW, Read C. Pathophysiology, Clinical Presentation, and Treatment of Psoriasis: A Review. JAMA. 2020;323(19):1945-60.

Skayem C, Taieb C, Halioua B, Baissac C, Saint Aroman M. Epidemiology of Psoriasis: A Worldwide Global Study. Acta Derm Venereol. 2025;105:adv42945.

Kohorst JJ, Kimball AB, Davis MD. Systemic associations of hidradenitis suppurativa. J Am Acad Dermatol. 2015;73(5 Suppl 1):S27-35.

Çelik MS, Aktaş H. The effect of IL-17 and IL-23 ınhibitors on hematological ınflammatory parameters in patients with psoriasis vulgaris. Ir J Med Sci. 2025;194(4):1329-34.

Gao S, Xie X, Fan L, Yu L. Efficacy and safety of IL-17, IL-12/23, and IL-23 inhibitors for psoriatic arthritis: a network meta-analysis of randomized controlled trials. Front Immunol. 2025;16:1654343.

Lee RA, Eisen DB. Treatment of hidradenitis suppurativa with biologic medications. J Am Acad Dermatol. 2015;73(5 Suppl 1):S82-8.

Narang J, Eversman A, Kalra M, Morgan F, Obi E, Russell ER, et al. Trends of Research Output of Allopathic Medical Students Matching Into Dermatology, 2007-2018. JAMA Dermatol. 2021;157(8):1–5.

Healy E, Brown SJ, Langan SM, Nicholls SG, Shams K, Reynolds NJ. Identification of translational dermatology research priorities in the U.K.: results of an electronic Delphi exercise. Br J Dermatol. 2015;173(5):1191-8.

Li Z, Koban KC, Schenck TL, Giunta RE, Li Q, Sun Y. Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends. J Clin Med. 2022;11(22):6826

Jairath N, Pahalyants V, Shah R, Weed J, Carucci JA, Criscito MC. Artificial Intelligence in Dermatology: A Systematic Review of Its Applications in Melanoma and Keratinocyte Carcinoma Diagnosis. Dermatol Surg. 2024;50(9):791-8.

Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29(8):1836-42.

Downloads

Published

2026-03-07

How to Cite

1.
Celik MS, Celik C. Research trends in Dermatology specialty theses: A bibliometric analysis of the national thesis center in Turkey (2020–2025). J Clin Trials Exp Investig [Internet]. 2026 Mar. 7 [cited 2026 Mar. 8];5(1):e18895284. Available from: https://jctei.com/index.php/jctei/article/view/197