## 8 Conclusions  
This study provides valuable insights into the accuracy and reliability of LLMs in predicting medical diagnoses based on Spanish textual descriptions of clinical cases. The findings highlight the potential of LLMs in healthcare applications but also underscore the need for careful selection of models, prompt techniques, and temperature settings to optimize diagnostic performance.  
Across all models and prompt techniques, low-temperature settings consistently produce higher-quality diagnostics that are clear, precise, and supported by strong clinical evidence, making them ideal for routine clinical use. High temperatures, while introducing more creative and speculative diagnostic possibilities, often compromise precision, clarity, and focus. The choice between low and high temperatures ultimately depends on the clinical context: low temperatures are best suited for focused, evidence-based diagnostics, while high temperatures are more appropriate for exploratory tasks requiring broader diagnostic consideration. The balance between these factors varies slightly by model, but the overall trends remain consistent across Gemini Pro, Mistral 8x22b, and Medichat.  
The choice of prompt technique significantly impacts the quality of diagnostic outputs, with zero-shot excelling in simplicity and clarity and RAG providing the most in-depth and evidence-based diagnostics. Each technique shows clear advantages and trade-offs, with temperature settings further amplifying these effects.  
Future research should focus on developing advanced models and evaluation frameworks to further enhance the application of LLMs in healthcare. Additionally, exploring hybrid models, refining prompt techniques, and investigating the integration of additional contextual information could provide further insights into optimizing the use of LLMs in clinical practice. By addressing these areas, we can continue to advance the field and harness the full potential of LLMs in improving patient outcomes and healthcare delivery.  
Conceptualization, J.D. and J.C.; methodology, J.D. and J.C.; software, J.D.; validation, J.D. and J.C.; investigation, J.D.; resources, J.C.; data curation, J.D.; writing--original draft preparation, J.D.; writing--review and editing, J.C.; supervision, J.C.; project administration, J.C. All authors have read and agreed to the published version of the manuscript.  
J.C. work was supported by the Torres Quevedo grant \"Asistente Virtual para la mejora del sistema de salud--Virtual Assistant for Better HealthCare-VA4 BHC\" (ref. PTQ2021-012147).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
All authors were employed by the company Top Health Tech. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
MDTI Multidisciplinary Digital Publishing Institute  
DOAJ Directory of open access journals  
LLM Large Language Model  
NLP Natural Language Processing  
ML Machine Learning  
RAG Retrieval-Augmented Generation