## 1 Introduction  
Symptom checkers based on traditional machine learning (ML) and deep learning (DL) approaches have become increasingly common in healthcare applications. Early systems, such as rule-based expert systems, relied on predefined algorithms and decision trees to suggest potential diagnoses based on input symptoms [1, 2]. While these systems provided some degree of automation, they were often limited in terms of their flexibility and precision, particularly in complex or ambiguous cases. As computational power advanced, more complex ML and DL techniques were introduced to improve the accuracy of symptom checkers. These methods, which include random forests, support vector machines, and neural networks, were trained on large datasets to predict diagnoses more effectively. However, they still suffer from several key limitations, primarily related to the need for extensive, domain-specific training and the risks of overfitting [3, 4]. The reliance on training data is one of the most significant constraints of ML and DL approaches. These models require large, high-quality labeled datasets to function optimally, often necessitating domain-specific knowledge to ensure their accuracy. In many clinical settings, obtaining sufficiently detailed and labeled data can be a daunting task [5]. Moreover, these systems often demand the input of a complete set of symptoms to make an accurate diagnosis, which may not always be available in real-world scenarios [6]. Despite advancements in ML and DL, symptom checkers still struggle with generalization and adaptability. Each model must be retrained when faced with a new set of conditions or medical specialties, limiting their flexibility. In contrast, large language models (LLMs), which have shown significant promise in a variety of natural language processing tasks, offer a new approach to medical diagnosis. Unlike traditional ML/DL models, LLMs are capable of leveraging vast amounts of text data without the need for extensive retraining [7; 8; 9; 10]. These notable improvements can be partly attributed to the adoption of methods that encode and manipulate text data using latent representations. Those methods embed text into high-dimensional vector spaces that capture the underlying semantics and structure of language. Thus, they can process incomplete information, adapt to various medical domains, and generate diagnostics in a conversational context, making them a promising alternative for developing more robust and generalizable symptom checkers [11]. These innovations have paved the way for novel patient care solutions, particularly in the diagnosis of diseases [12]. In this study, we focus on evaluating the effectiveness of LLMs in predicting diagnostics for cardiological diseases based on clinical cases in Spanish. The use of LLMs as symptom checkers has gained attention due to their potential to improve medical education and patient care. However, the performance of LLMs in diagnosing diseases in non-English languages and the impact of prompt techniques and temperature levels on their performance remain under-explored. In this paper, we aim to address these gaps by evaluating the impact of zero-shot, few-shot, and Retrieval-Augmented Generation (RAG) prompt techniques as well as temperature levels (low vs. high) on the quality of diagnostic predictions made by LLMs in Spanish cardiology cases. We evaluate three LLMs, namely Gemini Pro [13], Mixtral 8x22b [14], and Medichat Llama3 [[[https://huggingface.co/sethuiyer/Medichat-Llama3-88](https://huggingface.co/sethuiyer/Medichat-Llama3-88)]([https://huggingface.co/sethuiyer/Medichat-Llama3-88](https://huggingface.co/sethuiyer/Medichat-Llama3-88))], using two sets of clinical cases in Spanish [15; 16], accessed on 20 June 2024. Our results indicate that Gemini Pro and Mixtral 8x22b generally performed well across different temperatures and techniques, while Medichat Llama3 showed more variability, particularly with the few-shot prompting technique.