标题： 生成式人工智能模型中医疗安全信息的系统性分析 显示英文标题

标题： A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models

摘要： 生成式AI模型，包括大型语言模型（LLMs）和视觉语言模型（VLMs），被越来越多地用于解读医学图像和回答临床问题。 它们的回应通常包含不准确之处；因此，像医疗免责声明这样的安全措施至关重要，以提醒用户AI输出未经专业审核，也不能替代医疗建议。 本研究评估了从2022年到2025年的不同模型生成版本中，LLM和VLM输出中的免责声明存在情况。 使用500张乳腺X光片、500张胸部X光片、500张皮肤科图像和500个医学问题，对输出中的免责声明短语进行了筛查。 LLM和VLM输出中的医疗免责声明存在率分别从2022年的26.3%下降到2025年的0.97%，以及从2023年的19.6%下降到2025年的1.05%。 到2025年，大多数模型已不再显示免责声明。 随着公共模型变得越来越强大和权威，必须将免责声明作为一项保障措施实施，以适应每个输出的临床情境。 显示英文摘要

摘要： Generative AI models, including large language models (LLMs) and vision-language models (VLMs), are increasingly used to interpret medical images and answer clinical questions. Their responses often include inaccuracies; therefore, safety measures like medical disclaimers are critical to remind users that AI outputs are not professionally vetted or a substitute for medical advice. This study evaluated the presence of disclaimers in LLM and VLM outputs across model generations from 2022 to 2025. Using 500 mammograms, 500 chest X-rays, 500 dermatology images, and 500 medical questions, outputs were screened for disclaimer phrases. Medical disclaimer presence in LLM and VLM outputs dropped from 26.3% in 2022 to 0.97% in 2025, and from 19.6% in 2023 to 1.05% in 2025, respectively. By 2025, the majority of models displayed no disclaimers. As public models become more capable and authoritative, disclaimers must be implemented as a safeguard adapting to the clinical context of each output.