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Leveraging Generative Artificial Intelligence Models in Patient Education on Inferior Vena Cava Filters

Authors :
Som P. Singh
Aleena Jamal
Farah Qureshi
Rohma Zaidi
Fawad Qureshi
Source :
Clinics and Practice, Vol 14, Iss 4, Pp 1507-1514 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Background: 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. Methods: 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. Results: 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 (p = 8.70408 × 10−8) was found to be statistically significant albeit with priori power found to be low at 0.392. Conclusions: 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.

Details

Language :
English
ISSN :
20397283
Volume :
14
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Clinics and Practice
Publication Type :
Academic Journal
Accession number :
edsdoj.23cb309cf67e4d358fdc537ee9929ce9
Document Type :
article
Full Text :
https://doi.org/10.3390/clinpract14040121