Resumen
This study aimed to evaluate the potential of Large Language Models (LLMs) in healthcare diagnostics, specifically their ability to analyze symptom-based prompts and provide accurate diagnoses. The study focused on models including GPT-4, GPT-4o, Gemini, o1 Preview, and GPT-3.5, assessing their performance in identifying illnesses based solely on provided symptoms. Symptom-based prompts were curated from reputable medical sources to ensure validity and relevance. Each model was tested under controlled conditions to evaluate their diagnostic accuracy, precision, recall, and decision-making capabilities. Specific scenarios were designed to explore their performance in both general and high-stakes diagnostic tasks. Among the models, GPT-4 achieved the highest diagnostic accuracy, demonstrating strong alignment with medical reasoning. Gemini excelled in high-stakes scenarios requiring precise decision-making. GPT-4o and o1 Preview showed balanced performance, effectively handling real-time diagnostic tasks with a focus on both precision and recall. GPT-3.5, though less advanced, proved dependable for general diagnostic tasks. This study highlights the strengths and limitations of LLMs in healthcare diagnostics. While models such as GPT-4 and Gemini exhibit promise, challenges such as privacy compliance, ethical considerations, and the mitigation of inherent biases must be addressed. The findings suggest pathways for responsibly integrating LLMs into diagnostic processes to enhance healthcare outcomes.