## 6 Discussions and Conclusions  
As shown in the literature, numerous examples of the applicability of _LLM_s in the medical field have been presented. However, the long-term success of these technologies faces significant barriers, particularly in terms of data privacy and ethical challenges. Ensuring compliance with stringent data protection regulations such as GDPR and HIPAA remains a major concern, as healthcare data are highly sensitive. Furthermore, ethical issues, including potential algorithmic bias and the need for transparent decision making by AI, present considerable obstacles. Addressing these challenges will be crucial for the future scalability and reliability of _LLM_s in the healthcare context. To overcome these hurdles, it is essential that healthcare professionals and policymakers adopt precautionary approaches by developing clear guidelines for the ethical and regulatory use of _LLM_s. In particular, healthcare professionals should be trained to understand the limitations and potential of language models, ensuring that the use of these tools does not compromise the quality of clinical decisions. Policymakers, on the other hand, should establish regulations that protect patient privacy while simultaneously promoting technological innovation responsibly. Ethical considerations, such as managing algorithmic bias and ensuring transparency in automated decision-making processes, must be an integral part of healthcare policies. The results reveal a steadily growing trend in the adoption of _LLM_s, with three distinct periods identified, the most recent of which shows an exponential increase in publications. A significant proportion of the studies focuses on practical applications of _LLM_s in medicine, highlighting their increasing relevance and utility in real-world scenarios. Collaboration networks demonstrate the presence of central hubs, often located in major research institutions of global importance. However, notable contributions are also observed from smaller research centers, reflecting a diverse and distributed landscape of scientific efforts. At a national level, collaboration hubs are primarily concentrated in the United States and other English-speaking countries, although smaller nations also play a key role in specific collaborations, showcasing their impactful contributions to the field. Future work will extend the analysis of collaborations to identify research communities and investigate their significance, with the aim of uncovering underlying structures and dynamics that drive scientific advancements in the application of _LLM_s to medicine. Regarding future developments, the analysis of the literature could evolve into a more collaborative and dynamic approach, where researchers, professionals, and policymakers work together to explore the impact of _LLM_ usage in medicine. A deeper analysis of collaborations between various stakeholders over time could highlight significant changes in research dynamics, revealing how international alliances, technological innovations, and healthcare policies have influenced the evolution of _LLM_ technologies. Additionally, such an analysis could uncover how socio-political transformations have contributed to the adoption and direction of research on the use of _LLM_s in healthcare. Growing awareness of privacy, ethical, and security issues, as well as interest in advanced technological solutions, may manifest differently at a global level, leading to political and social implications that could shape the evolution of digital medicine. Analyzing publications in the healthcare field could reveal how regulatory approaches and public policies have responded to these developments, offering new insights to guide future research. By overcoming these challenges, the adoption of _LLM_s could profoundly transform healthcare systems, improving not only operational efficiency but also the quality and equity of treatments, while enabling a more adequate response to socio-political changes that influence both medicine and technology.