###### Abstract
There are also concerns about bias and privacy, which prevent these models from being widely used in healthcare. Not many studies have yet adequately analysed these issues. The literature [8; 9; 10; 11; 12] suggests that their user-friendly interface, wide accessibility, and advanced computational algorithms render them an exemplary supplement for medical trainees and early-career doctors. In this study, we rigorously assess the capabilities of the three most distinguished LLMs--ChatGPT-4, Google's PaLM, and Meta's LLaMA--in providing reliable, pertinent, and secure counsel in a spectrum of complex clinical scenarios. Our objective is to investigate their revolutionary impact on medical pedagogy, with the ultimate goal of enhancing the efficacy and customization of the educational experience for medical professionals. Recent advancements in LLMs and AI have also shown significant potential in emergency medicine [13], particularly in first aid scenarios. Studies have evaluated the ability of these technologies to provide life-saving instructions and their adherence to current medical guidelines. The readability and comprehensibility of the advice given by these models are also crucial, as they determine their utility to the general public. The COVID-19 pandemic has underscored the need for innovative approaches in medical training, given the reduced clinical exposure for medical students and junior doctors. LLMs, with their sophisticated algorithms and easy accessibility, are poised to serve as adjuncts in theoretical learning and clinical decision-making. This article examines the efficacy of nine different LLM in providing accurate and safe medical advice. The integration of Large Language Models (LLMs) such as ChatGPT, Google's PaLM, and Meta's LLaMA into the medical field, particularly as symptom checkers, represents a significant leap in digital healthcare. As highlighted by [PERSON] et al. [14], [PERSON] et al. [15], and [PERSON] et al. [16], these models have shown promise in enhancing medical education and patient engagement. The effectiveness of ChatGPT in medical education, explored by [PERSON] et al. [8] and [PERSON] et al. [12], underscores its potential in clinical training and decision support. [PERSON] & [PERSON] [6] provide insights into Google's PaLM capabilities in emergency scenarios, demonstrating its utility in delivering first aid instructions. A pivotal study by [PERSON] et al. [5] further emphasizes the diversity of LLMs' applications in healthcare. Their research offers an in-depth analysis comparing the responses of various virtual assistants, including ChatGPT, Google's PaLM, and others, to health-related queries. The study highlights the variation in the quality of information provided by these assistants, emphasizing the importance of accuracy and reliability in medical contexts. It points out that while some models excel in providing detailed and medically accurate responses, others may lack in consistency or adherence to medical guidelines. This comparison sheds light on the strengths and limitations of each model, providing a nuanced understanding of how LLMs can be optimized for healthcare applications. The findings of [PERSON] et al. [5] serve as a benchmark for evaluating the performance of LLMs in health-related inquiries. Their work underscores the need for ongoing assessment and improvement of these models to ensure they meet the high standards required for medical use. In this paper we investigate nine different publicly available LLM models and compare their capabilities in the diagnosis of 20 real cardiological disease cases. The main objective is to assess which type of model might work best without any specific fine-tuning, and to gain insight into the performance and efficiency of these models in the context of symptom checking, providing a comprehensive analysis of their diagnostic accuracy and reliability when facing a diverse range of real-world disease cases.