## 6 Conclusions and Future Work  
This paper describes the process and outcomes of customizing a large pre-trained language model from the general domain to the medical domain, resulting in MedicoBERT. Three specific tokenizers were developed for the Spanish medical literature vocabulary. The model was then trained on _Masked Language Modeling (MLM)_ and _Next Sentence Prediction (NSP)_ tasks. The obtained results confirm the successful adaptation of the model to medical language, validating the effectiveness of the process. The primary contribution of this work is the MedicoBERT language model, trained on a dataset of over 3 million texts from the Spanish medical literature. As a result, the MedicoBERTcoBERT model has comprehensive knowledge about diseases, drugs, and treatments, making it a valuable tool for various natural language processing tasks in the medical field. The model is highly adaptable and can be fine-tuned for diverse NLP tasks, such as medical document classification and clinical information extraction. Additionally, this paper details steps to enhance the fine-tuning process of a large language model (LLM) through calibration and hyperparameter optimization, significantly improving the model's efficiency in solving NLP tasks. Furthermore, a new approach to answering medical questions in Spanish is introduced, showing promising results compared to existing methods. In summary, this work indicates that advancements in adaptive learning techniques will lead to the development of more sophisticated large language models. These models will have the ability to interact naturally and intelligently across a wide range of tasks and domains, adapting to varied terminologies and vocabularies. This progress is driven by the potential and capabilities of LLMs and the active area of research in adaptive learning in NLP. Currently, the model demonstrates satisfactory performance in answering questions validated by experts in the medical domain. However, there is an ongoing objective to extend the scope of MedicoBERT and address potential challenges in the medical area. In future work, we intend to extend its application to other natural language processing tasks within the medical domain. In particular, we will concentrate on the identification of medical entities, including anatomical structures, diseases, and drugs. Additionally, we propose to enhance MedicoBERT's capabilities to encompass the extraction of semantic relations within the medical domain. This includes discerning causal relationships and associations between entities, such as the indication of a particular disease for a specific drug or the determination of a disease's relation to one or more anatomical parts. Furthermore, we will investigate the adaptation of the model for medical text classification tasks. The enhancement of MedicoBERT for the specified tasks entails the collection, adaptation, and processing of datasets comprising accurate and validated information. Furthermore, a novel approach for fine-tuning the model hyperparameters will be employed, utilizing a simulated annealing algorithm, which will require considerable computational time. By outlining these potential areas of research and development, this paper provides a roadmap for future work on the MedicoBERT model. Conceptualization, A.D.C.-R., J.A.R.-O., R.A.M.-G., J.P.C. and M.B. ; methodology, A.D.C.-R., J.A.R.-O. and J.P.C. ; software, J.P.C., R.A.M.-G. and J.A.R.-O. ; validation, J.A.R.-O. and A.D.C.-R.; formal analysis, J.P.C. and J.A.R.-O. ; investigation, J.P.C. ; resources, J.P.C. and R.A.M.-G.; data curation, J.P.C., J.A.R.-O. and M.B. ; writing--original draft preparation, J.P.C. ; writing--review and editing, A.D.C.-R., J.A.R.-O., R.A.M.-G. and M.B. ; visualization, J.P.C. ; supervision, J.A.R.-O. and A.D.C.-R.; funding acquisition, J.A.R.-O., M.B. and A.D.C.-R. All authors have read and agreed to the published version of the manuscript. The present work was funded by the Consejo Nacional de Humanidades, Ciencia y Tecnologia (CONAHCYT) Mexico under scholarship No.