Singh Som P, Jamal Aleena, Qureshi Farah, Zaidi Rohma, Qureshi Fawad
Department of Internal Medicine, University of Missouri Kansas City School of Medicine, Kansas City, MO 64108, USA.
Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA.
Clin Pract. 2024 Jul 30;14(4):1507-1514. doi: 10.3390/clinpract14040121.
: Inferior Vena Cava (IVC) filters have become an advantageous treatment modality for patients with venous thromboembolism. As the use of these filters continues to grow, it is imperative for providers to appropriately educate patients in a comprehensive yet understandable manner. Likewise, generative artificial intelligence models are a growing tool in patient education, but there is little understanding of the readability of these tools on IVC filters. : This study aimed to determine the Flesch Reading Ease (FRE), Flesch-Kincaid, and Gunning Fog readability of IVC Filter patient educational materials generated by these artificial intelligence models. : The ChatGPT cohort had the highest mean Gunning Fog score at 17.76 ± 1.62 and the lowest at 11.58 ± 1.55 among the Copilot cohort. The difference between groups for Flesch Reading Ease scores ( = 8.70408 × 10) was found to be statistically significant albeit with priori power found to be low at 0.392. : The results of this study indicate that the answers generated by the Microsoft Copilot cohort offers a greater degree of readability compared to ChatGPT cohort regarding IVC filters. Nevertheless, the mean Flesch-Kincaid readability for both cohorts does not meet the recommended U.S. grade reading levels.
下腔静脉（IVC）滤器已成为静脉血栓栓塞患者的一种有利治疗方式。随着这些滤器的使用持续增加，医疗服务提供者必须以全面且易懂的方式对患者进行适当的教育。同样，生成式人工智能模型在患者教育中是一种日益重要的工具，但对于这些工具关于IVC滤器的可读性了解甚少。本研究旨在确定由这些人工智能模型生成的IVC滤器患者教育材料的弗莱什易读性（FRE）、弗莱什-金凯德可读性和冈宁雾度可读性。在Copilot队列中，ChatGPT队列的平均冈宁雾度得分最高，为17.76±1.62，而在Copilot队列中最低，为11.58±1.55。发现弗莱什易读性得分组间差异（=8.70408×10）具有统计学意义，尽管先验检验效能较低，为0.392。本研究结果表明，就IVC滤器而言，与ChatGPT队列相比，Microsoft Copilot队列生成的答案具有更高的可读性。然而，两个队列的平均弗莱什-金凯德可读性均未达到美国推荐的年级阅读水平。
