The integration of Large Language Models (LLMs) into healthcare holds significant potential to enhance diagnostic accuracy and support medical treatment planning. These AI-driven systems can analyze vast datasets, assisting clinicians in identifying diseases, recommending treatments, and predicting patient outcomes. This study evaluates the performance of a range of contemporary LLMs, including both open-source and closed-source models, on the 2024 Portuguese National Exam for medical specialty access (PNA), a standardized medical knowledge assessment. Our results highlight considerable variation in accuracy and cost-effectiveness, with several models demonstrating performance exceeding human benchmarks for medical students on this specific task. We identify leading models based on a combined score of accuracy and cost, discuss the implications of reasoning methodologies like Chain-of-Thought, and underscore the potential for LLMs to function as valuable complementary tools aiding medical professionals in complex clinical decision-making.

本研究针对大型语言模型（LLMs）在医疗领域中的应用，评估其对诊断精度和治疗规划支持的潜力。研究发现，某些LLMs的表现优于医学生的人类基准，尤其是在2024年葡萄牙国家医疗专业考试中，表明其作为医疗决策辅助工具的巨大潜力。

大型语言模型在支持医疗诊断与治疗中的表现