Rapid integration of large language models (LLMs) in health care is sparking global discussion about their potential to revolutionize health care quality and accessibility. At a time when improving health care quality and access remains a critical concern for countries worldwide, the ability of these models to pass medical examinations is often cited as a reason to use them for medical training and diagnosis. However, the impact of their inevitable use as a self-diagnostic tool and their role in spreading healthcare misinformation has not been evaluated. This study aims to assess the effectiveness of LLMs, particularly ChatGPT, from the perspective of an individual self-diagnosing to better understand the clarity, correctness, and robustness of the models. We propose the comprehensive testing methodology evaluation of LLM prompts (EvalPrompt). This evaluation methodology uses multiple-choice medical licensing examination questions to evaluate LLM responses. We use open-ended questions to mimic real-world self-diagnosis use cases, and perform sentence dropout to mimic realistic self-diagnosis with missing information. Human evaluators then assess the responses returned by ChatGPT for both experiments for clarity, correctness, and robustness. The results highlight the modest capabilities of LLMs, as their responses are often unclear and inaccurate. As a result, medical advice by LLMs should be cautiously approached. However, evidence suggests that LLMs are steadily improving and could potentially play a role in healthcare systems in the future. To address the issue of medical misinformation, there is a pressing need for the development of a comprehensive self-diagnosis dataset. This dataset could enhance the reliability of LLMs in medical applications by featuring more realistic prompt styles with minimal information across a broader range of medical fields.

翻译：大型语言模型在医疗保健领域的快速整合正引发全球性讨论，探讨其革新医疗质量与可及性的潜力。在提升医疗质量与可及性仍是各国核心关切的当下，这些模型通过医学考试的能力常被引证作为其应用于医学培训与诊断的理由。然而，其作为自我诊断工具不可避免的使用及其在传播医疗误信息中的作用尚未得到评估。本研究旨在从个体自我诊断的视角评估大型语言模型（特别是ChatGPT）的有效性，以深入理解模型的清晰性、正确性与鲁棒性。我们提出了综合性测试方法——大型语言模型提示词评估（EvalPrompt）。该评估方法采用多项选择题形式的医疗执照考试题目来评估大型语言模型的应答。我们通过开放式问题模拟真实世界的自我诊断场景，并执行语句随机丢弃以模拟信息缺失的现实自我诊断情境。随后，由人类评估者对ChatGPT在两项实验中生成的应答进行清晰度、准确性与鲁棒性评估。结果突显了大型语言模型的有限能力，其应答往往存在表述不清与内容失准的问题。因此，对大型语言模型提供的医疗建议应持审慎态度。然而，有证据表明大型语言模型正在稳步改进，未来可能在医疗系统中发挥作用。为应对医疗误信息问题，亟需开发综合性的自我诊断数据集。该数据集可通过涵盖更广泛医学领域、采用信息极简化的拟真提示风格，从而提升大型语言模型在医疗应用中的可靠性。