## 3 Methods
### Bibliometric Indicators  
This review uses several bibliometric indicators to analyze temporal trends, geographical distribution, and collaborative networks. We will conduct three types of analyses: temporal-, geographical-, and collaboration-based analyses. Temporal analysis will focus on the evolution of the use of _LLM_s, geographical analysis will focus on the spread of _LLM_s over the world and, finally, the collaboration network analysis will explore interactions between various entities (authors, research centers, or nations) in the adoption of _LLM_s in medicine. To summarize, this paper aims to answer some of the following questions.  
* Temporal analysis. To measure the growth rate of research outputs and identify significant technological advancements or pivotal years in _LLM_ adoption.
* When were _LLM_s introduced into the medical field, and how has their application evolved over time?
* What moments of innovation and technological advancements have driven the adoption of these models in the healthcare sector?
* How have the areas of application (diagnosis, treatment, medical research, healthcare management, etc.) changed over the years?
* Have seasonal trends or specific events emerged that accelerated the adoption of _LLM_s in the medical field?
* Geographical analysis. Mapping authors' affiliations to assess regional adoption and the influence of socio-economic factors.
* In which regions or countries are _LLM_s most widely used in medicine?
* Are there significant differences in the adoption of _LLM_s in medicine between developed and developing countries?
* How are _LLM_s utilized in various medical specialties across different geographical areas (e.g., oncology, cardiology, and emergency medicine)?
* What cultural differences or healthcare policies influence the adoption and use of _LLM_s in medicine?
* Collaboration network analysis.
* Who are the main actors involved in researching and implementing _LLM_s in medicine?
* What international partnerships or cross-sector collaborations (e.g., between medical research institutes and technology companies) have emerged in the adoption of _LLM_s?
* Are there collaborative networks that share resources, data, and knowledge to optimize the use of _LLM_s in the medical field?
* How do healthcare policies and legal regulations influence the creation and effectiveness of these networks?
* Thematic analysis.
* What are the primary applications of _LLM_s in the healthcare domain? This question identifies the specific tasks where _LLM_s are applied, such as medical text summarization, clinical decision support, diagnosis prediction, electronic health record (EHR) management, patient communication, and medical research analysis.
* What are the key benefits of using _LLM_s in healthcare? The analysis highlights the advantages of _LLM_s adoption, including improved efficiency in processing medical data, enhanced diagnostic accuracy, real-time patient support, and reduced administrative workload for healthcare professionals.
* What challenges and limitations are encountered in the adoption of _LLM_s in healthcare?
* How do _LLM_s impact patient outcomes and healthcare workflows?
* What future opportunities and trends can be identified for _LLM_s in health? In conclusion, this paper is relevant for understanding the adoption and evolution of large language models in the medical field, as it will provide valuable insights on (1) the temporal dynamics and global trends in the adoption of these technologies, (2) regional differences in the implementation of _LLM_s, highlighting areas of significant development and those that could benefit from increased efforts, (3) international and cross-sector collaborations that can foster innovation and the integration of _LLM_s into the healthcare system, and finally, (4) thematic aspects for a comprehensive understanding of the role, challenges, and potential of _LLM_s in revolutionizing healthcare practices.