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Assessing the quality and readability of patient education materials on chemotherapy cardiotoxicity from artificial intelligence chatbots: An observational cross-sectional study.

作者信息

Stephenson-Moe Christoph A, Behers Benjamin J, Gibons Rebecca M, Behers Brett M, Jesus Herrera Laura De, Anneaud Djhemson, Rosario Manuel A, Wojtas Caroline N, Bhambrah Samantha, Hamad Karen M

机构信息

Florida State University College of Medicine, Tallahassee, FL.

Florida State University Internal Medicine Residency, Sarasota Memorial Hospital, Sarasota, FL.

出版信息

Medicine (Baltimore). 2025 Apr 11;104(15):e42135. doi: 10.1097/MD.0000000000042135.


DOI:10.1097/MD.0000000000042135
PMID:40228277
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11999455/
Abstract

Artificial intelligence (AI) and the introduction of Large Language Model (LLM) chatbots have become a common source of patient inquiry in healthcare. The quality and readability of AI-generated patient education materials (PEM) is the subject of many studies across multiple medical topics. Most demonstrate poor readability and acceptable quality. However, an area yet to be investigated is chemotherapy-induced cardiotoxicity. This study seeks to assess the quality and readability of chatbot created PEM relative to chemotherapy-induced cardiotoxicity. We conducted an observational cross-sectional study in August 2024 by asking 10 questions to 4 chatbots: ChatGPT, Microsoft Copilot (Copilot), Google Gemini (Gemini), and Meta AI (Meta). The generated material was assessed for readability using 7 tools: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG) Index, Automated Readability Index (ARI), and FORCAST Grade Level. Quality was assessed using modified versions of 2 validated tools: the Patient Education Materials Assessment Tool (PEMAT), which outputs a 0% to 100% score, and DISCERN, a 1 (unsatisfactory) to 5 (highly satisfactory) scoring system. Descriptive statistics were used to evaluate performance and compare chatbots amongst each other. Mean reading grade level (RGL) across all chatbots was 13.7. Calculated RGLs for ChatGPT, Copilot, Gemini and Meta were 14.2, 14.0, 12.5, 14.2, respectively. Mean DISCERN scores across the chatbots was 4.2. DISCERN scores for ChatGPT, Copilot, Gemini, and Meta were 4.2, 4.3, 4.2, and 3.9, respectively. Median PEMAT scores for understandability and actionability were 91.7% and 75%, respectively. Understandability and actionability scores for ChatGPT, Copilot, Gemini, and Meta were 100% and 75%, 91.7% and 75%, 90.9% and 75%, and 91.7% and 50%, respectively. AI chatbots produce high quality PEM with poor readability. We do not discourage using chatbots to create PEM but recommend cautioning patients about their readability concerns. AI chatbots are not an alternative to a healthcare provider. Furthermore, there is no consensus on which chatbots create the highest quality PEM. Future studies are needed to assess the effectiveness of AI chatbots in providing PEM to patients and how the capabilities of AI chatbots are changing over time.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/930b/11999455/ba213752cce0/medi-104-e42135-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/930b/11999455/82fc0c0b6107/medi-104-e42135-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/930b/11999455/ba213752cce0/medi-104-e42135-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/930b/11999455/82fc0c0b6107/medi-104-e42135-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/930b/11999455/ba213752cce0/medi-104-e42135-g002.jpg

相似文献

[1]
Assessing the quality and readability of patient education materials on chemotherapy cardiotoxicity from artificial intelligence chatbots: An observational cross-sectional study.

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本文引用的文献

[1]
Quality of Chatbot Information Related to Benign Prostatic Hyperplasia.

Prostate. 2025-2

[2]
Assessing the Quality of Patient Education Materials on Cardiac Catheterization From Artificial Intelligence Chatbots: An Observational Cross-Sectional Study.

Cureus. 2024-9-23

[3]
Assessing the readability, reliability, and quality of artificial intelligence chatbot responses to the 100 most searched queries about cardiopulmonary resuscitation: An observational study.

Medicine (Baltimore). 2024-5-31

[4]
Chatbots talk Strabismus: Can AI become the new patient Educator?

Int J Med Inform. 2024-11

[5]
Assessing the Readability of Patient Education Materials on Cardiac Catheterization From Artificial Intelligence Chatbots: An Observational Cross-Sectional Study.

Cureus. 2024-7-4

[6]
The quality and readability of patient information provided by ChatGPT: can AI reliably explain common ENT operations?

Eur Arch Otorhinolaryngol. 2024-11

[7]
Talking technology: exploring chatbots as a tool for cataract patient education.

Clin Exp Optom. 2025-1

[8]
The Readability and Quality of Web-Based Patient Information on Nasopharyngeal Carcinoma: Quantitative Content Analysis.

JMIR Form Res. 2023-11-27

[9]
Assessment of Artificial Intelligence Chatbot Responses to Top Searched Queries About Cancer.

JAMA Oncol. 2023-10-1

[10]
Head-to-Head Comparison of ChatGPT Versus Google Search for Medical Knowledge Acquisition.

Otolaryngol Head Neck Surg. 2024-6

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