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Assessing the Readability of Patient Education Materials on Cardiac Catheterization From Artificial Intelligence Chatbots: An Observational Cross-Sectional Study.
Behers, Benjamin J; Vargas, Ian A; Behers, Brett M; Rosario, Manuel A; Wojtas, Caroline N; Deevers, Alexander C; Hamad, Karen M.
Afiliação
  • Behers BJ; Department of Internal Medicine, Sarasota Memorial Hospital, Sarasota, USA.
  • Vargas IA; Department of Internal Medicine, Sarasota Memorial Hospital, Sarasota, USA.
  • Behers BM; Department of Clinical Research, University of South Florida Morsani College of Medicine, Tampa, USA.
  • Rosario MA; Department of Internal Medicine, Sarasota Memorial Hospital, Sarasota, USA.
  • Wojtas CN; Department of Internal Medicine, Sarasota Memorial Hospital, Sarasota, USA.
  • Deevers AC; Department of Clinical Research, University of Florida, Gainesville, USA.
  • Hamad KM; Department of Internal Medicine, Sarasota Memorial Hospital, Sarasota, USA.
Cureus ; 16(7): e63865, 2024 Jul.
Article em En | MEDLINE | ID: mdl-39099896
ABSTRACT

BACKGROUND:

Artificial intelligence (AI) is a burgeoning new field that has increased in popularity over the past couple of years, coinciding with the public release of large language model (LLM)-driven chatbots. These chatbots, such as ChatGPT, can be engaged directly in conversation, allowing users to ask them questions or issue other commands. Since LLMs are trained on large amounts of text data, they can also answer questions reliably and factually, an ability that has allowed them to serve as a source for medical inquiries. This study seeks to assess the readability of patient education materials on cardiac catheterization across four of the most common chatbots ChatGPT, Microsoft Copilot, Google Gemini, and Meta AI.

METHODOLOGY:

A set of 10 questions regarding cardiac catheterization was developed using website-based patient education materials on the topic. We then asked these questions in consecutive order to four of the most common chatbots ChatGPT, Microsoft Copilot, Google Gemini, and Meta AI. The Flesch Reading Ease Score (FRES) was used to assess the readability score. Readability grade levels were assessed using six tools 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.

RESULTS:

The mean FRES across all four chatbots was 40.2, while overall mean grade levels for the four chatbots were 11.2, 13.7, 13.7, 13.3, 11.2, and 11.6 across the FKGL, GFI, CLI, SMOG, ARI, and FORCAST indices, respectively. Mean reading grade levels across the six tools were 14.8 for ChatGPT, 12.3 for Microsoft Copilot, 13.1 for Google Gemini, and 9.6 for Meta AI. Further, FRES values for the four chatbots were 31, 35.8, 36.4, and 57.7, respectively.

CONCLUSIONS:

This study shows that AI chatbots are capable of providing answers to medical questions regarding cardiac catheterization. However, the responses across the four chatbots had overall mean reading grade levels at the 11th-13th-grade level, depending on the tool used. This means that the materials were at the high school and even college reading level, which far exceeds the recommended sixth-grade level for patient education materials. Further, there is significant variability in the readability levels provided by different chatbots as, across all six grade-level assessments, Meta AI had the lowest scores and ChatGPT generally had the highest.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Cureus Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Cureus Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Estados Unidos