## 1 Introduction  
The aim of this paper is to explore an emerging and complex topic: the use of large language models (_LLM_) in the field of e-health through the analysis of publications. The main goal is to provide a general overview, identifying the main areas of interest and the most relevant research trends, also adding some geographical and topological deepening. An _LLM_ is a type of artificial intelligence model, specifically a language model, trained on vast amounts of textual data. These models use deep learning techniques, particularly transformer neural networks, to analyze, understand, and generate natural language text. _LLM_ are designed to predict words, sentences, or paragraphs and are capable of performingmultiple tasks, including answering questions on various topics, generating coherent and creative texts (such as articles, stories, or poems), translating different languages, and summarizing long documents. More advanced applications also extend to understanding and analyzing the sentiment of a text. The use of large language models (_LLM_s) in healthcare is rapidly transforming various aspects of medical practice and research. Numerous benefits have been achieved using _LLM_s in healthcare, particularly with time savings by automating documentation and repetitive tasks. Additionally, a significant advantage is the ability to support diagnostics by integrating clinical guidelines and extensive datasets, leading to an enhancement of results. Finally, _LLM_s enable the delivery of health information and services remotely through AI-driven applications. Large language models (_LLM_s) are transforming healthcare by offering innovative solutions across a range of applications. One key area is Clinical Decision Support, where _LLM_s provide evidence-based recommendations, diagnostic suggestions, and treatment options, enhancing clinical efficiency and accuracy [1]. Another impactful use is in Medical Documentation and Summarization, where _LLM_s automate documentation tasks by transcribing physician-patient interactions and generating structured summaries, significantly reducing the administrative burden on healthcare providers [2]. _LLM_s also contribute to improved Patient Interaction and Virtual Assistants, powering chatbots and virtual health assistants that offer general health information, schedule appointments, and send medication reminders [3;4]. In the field of Personalized Medicine, these models analyze patient-specific data to deliver tailored treatment recommendations based on genetic, clinical, and lifestyle information [5]. Lastly, Health Education benefits from _LLM_s by creating interactive educational content that simplifies complex medical concepts for both professionals and patients [6]. Together, these applications illustrate the transformative potential of _LLM_s in healthcare, though challenges in privacy, bias, and interpretability remain critical for future advancements. However, several challenges remain, especially in the domains of data privacy and security, bias and fairness, and the interpretability of results, which remains a critical issue. A scoping review on _LLM_s in the healthcare field is essential to outline the potential and challenges of this emerging technology, supporting informed and responsible implementation in clinical practice and research. This approach allows for exploring the scope, nature, and reach of the existing literature, identifying the main areas of application (such as diagnosis, education, and patient support), and emerging trends in research. _LLM_s present both promises and risks. On the one hand, they can improve clinical efficiency, support decision making, and reduce errors. On the other hand, they raise ethical issues, data bias concerns, and patient safety worries. A scoping review helps to clarify these dynamics, providing a solid foundation for new research questions dealing with health, computer science, and social matters.