Improving healthcare quality and access remains a critical concern for countries worldwide. Consequently, the rise of large language models (LLMs) has erupted a wealth of discussion around healthcare applications among researchers and consumers alike. While the ability of these models to pass medical exams has been used to argue in favour of their use in medical training and diagnosis, the impact of their inevitable use as a self-diagnostic tool and their role in spreading healthcare misinformation has not been evaluated. In this work, we critically evaluate LLMs' capabilities from the lens of a general user self-diagnosing, as well as the means through which LLMs may aid in the spread of medical misinformation. To accomplish this, we develop a testing methodology which can be used to evaluate responses to open-ended questions mimicking real-world use cases. In doing so, we reveal that a) these models perform worse than previously known, and b) they exhibit peculiar behaviours, including overconfidence when stating incorrect recommendations, which increases the risk of spreading medical misinformation.

翻译：改善医疗质量与可及性仍是各国关注的关键问题。因此，大语言模型的兴起引发了研究人员和消费者围绕医疗应用的广泛讨论。尽管这些模型通过医学考试的能力被用于论证其在医学培训和诊断中的应用价值，但其作为自我诊断工具不可避免的使用及其在传播医疗错误信息方面的作用尚未得到评估。本研究从普通用户自我诊断的视角，对大语言模型的能力进行了批判性评估，并分析了其可能助长医疗错误信息传播的途径。为此，我们开发了一套测试方法，用于评估模型对模拟真实使用场景的开放式问题的回答。通过该方法，我们发现：a) 这些模型的实际表现低于此前认知，b) 它们表现出独特的行为特征，包括在给出错误建议时过度自信，这增加了医疗错误信息传播的风险。