Mehmet Beşir DEMİRBAŞ, Aslı Nur SAVAŞ, Birkan TAPAN
Demiroğlu Bilim Üniversitesi Florence Nightingale Tıp Dergisi - 2026;12(1):1-10
Objectives: This study aims to systematically identify, classify, and evaluate generative artificial intelligence (GAI) tools used in healthcare. The research focuses on examining the application areas of these tools in both clinical and administrative contexts and analyzing their technological and operational characteristics. Materials and methods: A scoping review methodology was employed following the framework proposed by Arksey and O'Malley. Data were collected from academic databases, technology platforms, and publicly available sources to identify GAI tools related to healthcare. After applying the inclusion criteria, a dataset of 80 GAI tools developed between 2010 and 2025 was compiled. The collected data were analyzed using descriptive statistics and content analysis to classify the tools according to their application domains, development origins, platform availability, and pricing models. Results: The findings indicate that the majority of GAI tools in healthcare were developed in the United States, followed by other technologically advanced countries. The tools are widely used in areas such as medical imaging, clinical documentation, decision support systems, and drug discovery. Additionally, most of the identified tools operate through web-based platforms, while many follow commercial or freemium pricing models, reflecting the increasing commercialization of digital health technologies. Conclusion: Generative AI technologies have significant potential to transform healthcare systems by improving diagnostic accuracy, supporting clinical decision-making, enhancing operational efficiency, and enabling personalized medicine approaches. However, issues related to ethics, data privacy, regulatory frameworks, and equitable access must be carefully addressed to ensure the sustainable and responsible integration of GAI into healthcare services.
