Mapping Emerging Scientific Trends in Chronic Skin Disorders Using Machine Learning-Based Bibliometrics

Chronic dermatologic diseases are characterized by pathophysiologic complexity and the existence of many unmet patient management needs that can contribute to treatment failure, with poor adherence being a major issue. This study aims to identify key topics in this field, using the Web of Science database. To perform this analysis, tools such as VOSviewer, Bibliometrix, and Excel were used. A Python script leveraging machine learning algorithms was developed to standardize terminology. The initial search yielded 35,373 documents, which were then refined to 12,952 publications spanning 1975 to 2024 through parameter optimization. The study found an increasing interest in this research domain, with a notable surge in 2019. The analysis identified the United States, Germany, and England as the most prolific countries in terms of scientific output. Canada ranked sixth in total document production, but its documents received the highest average citations, reflecting a significant impact. Normalization analysis revealed Italy as the most specialized country in chronic skin disease research relative to total national research output. Trend analysis revealed an evolution in research topics, particularly after 2020, with a growing focus on personalized treatment methods and long-term treatment outcomes. The study highlighted international collaboration, especially among countries with cultural or regional connections, such as those within the European Union. It underscores the growing need for continuous updates and the increasing global focus on chronic skin diseases, highlighting the critical role of staying current with emerging trends to drive advancements in treatment and patient care.