Akademik Veri Yönetim Sistemi
IEEE Access, cilt.13, ss.157026-157043, 2025 (SCI-Expanded)
Recent advances in generative artificial intelligence (GenAI) have made large language models (LLMs) transformative tools in the healthcare industry. Powered by deep learning, these models show great potential in many medical applications such as clinical decision support systems, biomedical text mining and personalized patient care. However, while research on the use of LLMs in medicine is growing rapidly, there is a need to systematically analyze the key themes, emerging trends and challenges in this field. This study aims to identify dominant themes, track research trends and identify gaps in the literature by utilizing a large dataset of scientific publications in the medical field. A total of 3,941 academic publications related to the use of LLMs in medicine were retrieved from the Scopus database, covering the period from 2023 to 2024. Accordingly, the BERTopic method, an advanced topic modeling technique, is used to analyze and classify publications on LLMs applications in medicine. The findings show that LLMs are mostly concentrated in the fields of radiology, ophthalmology and mental health and that these models have the potential to support clinical processes. In particular, LLMs have been found to have a great impact on the analysis of medical imaging reports, are used for early diagnosis of ophthalmologic diseases, and are prominent in depression and suicide risk assessments in mental health. This study provides valuable insights for healthcare professionals, GenAI researchers, and policy makers, laying a solid foundation for future research and strategic applications for the use of LLMs in medicine. While emphasizing the transformative impact of LLMs in healthcare, it also highlights the necessity of responsible GenAI applications.