■全球对医疗保健的需求正在增加,在获取资源方面存在显著差异,尤其是在亚洲,非洲,和拉丁美洲。人工智能(AI)技术的快速发展,例如OpenAI的ChatGPT,在彻底改变医疗保健方面表现出了希望。然而,潜在的挑战,包括需要专门的医疗培训,隐私问题,语言偏见,需要注意。
■为了评估ChatGPT在中英文环境中的适用性和局限性,我们设计了一个实验,评估其在中国2022年国家医学许可考试(NMLE)中的表现。对于标准化评估,我们使用了NMLE的综合书面部分,由双语专家翻译成英语。所有问题都输入了ChatGPT,提供了选择它们的答案和原因。使用李克特量表评估“信息质量”的回答。
■ChatGPT显示中文的正确回答率为81.25%,英文问题的正确回答率为86.25%。Logistic回归分析表明,问题的难度和主题都不是AI错误的重要因素。Brier得分,指示预测准确性,中文为0.19,英文为0.14,表明良好的预测性能。英语回答的平均质量分数是优秀的(4.43分),略高于中国人(4.34分)。
虽然像ChatGPT这样的AI语言模型显示了对全球医疗保健的承诺,语言偏见是一个关键挑战。确保此类技术受到严格的培训,并对多种语言和文化敏感至关重要。进一步研究AI在医疗保健中的作用,特别是在资源有限的地区,是有保证的。
UNASSIGNED: The demand for healthcare is increasing globally, with notable disparities in access to resources, especially in Asia, Africa, and Latin America. The rapid development of Artificial Intelligence (AI) technologies, such as OpenAI\'s ChatGPT, has shown promise in revolutionizing healthcare. However, potential challenges, including the need for specialized medical training, privacy concerns, and language bias, require attention.
UNASSIGNED: To assess the applicability and limitations of ChatGPT in Chinese and English settings, we designed an experiment evaluating its performance in the 2022 National Medical Licensing Examination (NMLE) in China. For a standardized evaluation, we used the comprehensive written part of the NMLE, translated into English by a bilingual expert. All questions were input into ChatGPT, which provided answers and reasons for choosing them. Responses were evaluated for \"information quality\" using the Likert scale.
UNASSIGNED: ChatGPT demonstrated a correct response rate of 81.25% for Chinese and 86.25% for English questions. Logistic regression analysis showed that neither the difficulty nor the subject matter of the questions was a significant factor in AI errors. The Brier Scores, indicating predictive accuracy, were 0.19 for Chinese and 0.14 for English, indicating good predictive performance. The average quality score for English responses was excellent (4.43 point), slightly higher than for Chinese (4.34 point).
UNASSIGNED: While AI language models like ChatGPT show promise for global healthcare, language bias is a key challenge. Ensuring that such technologies are robustly trained and sensitive to multiple languages and cultures is vital. Further research into AI\'s role in healthcare, particularly in areas with limited resources, is warranted.