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Article Mapping the Literature on Thermal Tourism: A Bibliometric and Content Analysis(Conscientia Beam, 2025) Alqaifi, Faten; Tengilimoglu, Dilaver; Aras, Ilknur ArslanThis study explores and maps the existing literature on thermal tourism to provide comprehensive insights and inform future research directions. The research design is based on a bibliometric analysis of 48 documents published between 2013 and 2023 and indexed in the Web of Science database. Data were processed and visualized using Microsoft Excel and Bibliometrix, followed by a content analysis of 42 English-language articles to capture thematic developments in the field. The findings indicate that, despite a temporary decline during the COVID-19 pandemic, publications on thermal tourism have shown strong growth and recovery, with an annual growth rate of 46.65%. The most significant contributions originated from Portugal, China, Spain, Taiwan, Turkey, and Japan, with research disseminated across 37 journals. Four central themes were identified: (1) tourist behaviors and satisfaction, (2) demand and motivations, (3) quality and resource management, and (4) strategic and sustainable development. Among these, tourist behaviors and satisfaction emerged as the most prominent research area, representing 38.10% of the analyzed literature. This paper addresses a gap in the literature by mapping the knowledge landscape in the relatively underexplored field of thermal tourism, highlighting its growth potential and proposing a future research agenda. The practical implications suggest that recognizing these trends and themes can help policymakers, industry stakeholders, and academics develop strategies to enhance sustainable practices and expand opportunities in thermal tourism.Review Citation - WoS: 2Citation - Scopus: 2Artificial intelligence's impact on oral healthcare in terms of clinical outcomes: a bibliometric analysis(Emerald Group Publishing Ltd, 2024) AlQaifi, Faten; Tengilimoglu, Dilaver; Aras, Ilknur ArslanPurpose - This study provides a comprehensive overview of the impact of artificial intelligence (AI) applications on oral healthcare, focusing on clinical outcomes. Design/methodology/approach - A systematic approach was used to gather articles from databases such as Scopus, ScienceDirect, PubMed, Web of Science and Google Scholar from 2010 to 2024. The selection criteria included articles published in English, focusing solely on clinical applications of AI in dentistry. Articles such as conference proceedings, editorial material and personal opinions were excluded. The articles were analyzed and visualized using Rayyan software, Microsoft Excel and VOSviewer. Findings - Results indicate that 120 publications were authored by 58 scholars from 92 institutions across 29 countries, with a notable surge since 2018. This analysis showed the significant emphasis on the use of deep learning, demonstrating its high accuracy and performance in oral healthcare, often exceeding that of dentists. It also proved that even though AI is sometimes seen as an auxiliary tool, many studies revealed that AI has a performance near dental professionals' levels. Findings concluded that the majority of studies indicate that AI is generating better clinical outcomes in oral healthcare. Practical implications - This study provides dental professionals with insights on integrating AI for better diagnosis and treatment. Policymakers and healthcare institutions can use these findings to inform AI adoption and training strategies. Originality/value - It presents novel and valuable findings that can benefit various stakeholders by shedding light on the present scenario and potential future paths of AI integration in oral healthcare, contributing to its overall advancement.
