e.g. mhealth
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- 20 Journal of Medical Internet Research
- 13 JMIR Formative Research
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- 5 Interactive Journal of Medical Research
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Since then, the readability and quality of online health content accessed by patients have remained a key concern for clinicians, leading to numerous studies on the topic [7-10]. These studies examine various factors, but one of the most consistent is readability—specifically, the grade level at which the material is written.
JMIR Form Res 2025;9:e68000
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Recent studies have shown that AI-generated texts can simplify complex medical information and improve readability, outperforming traditional web-based materials [14-16]. By enhancing clarity and accessibility, AI-generated texts have the potential to reduce health disparities and ensure equitable access to vital health information, particularly during public health crises such as the mpox outbreak.
JMIR AI 2025;4:e70604
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Readability of Online Patient Educational Materials for Rosacea: Systematic Web Search and Analysis
Adhering to these readability standards is essential for improving patient comprehension and ensuring that dermatological resources are accessible to a wider audience. In this study, we assess the readability of available online educational material dedicated to the management and treatment of rosacea.
A web search was conducted on April 25, 2025, using Google with the query “Rosacea Patient Information.”
JMIR Form Res 2025;9:e67916
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We performed an in-depth linguistic analysis to evaluate the readability and complexity of the AI-generated responses. To achieve this, 2 main readability scores, the Flesch Reading Ease Score (FRES) and the Flesch–Kincaid Grade Level (FKGL), were computed using standard formulas. FRES evaluates text complexity on a scale from 0 to 100, where lower scores suggest more difficult reading.
FKGL approximates the US education grade level required to comprehend the text.
JMIR Form Res 2025;9:e73642
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Readability was assessed by averaging the following eight readability formulas: Automated Readability Index, Flesch Reading Ease Formula, Gunning Fog Index, Flesch-Kincaid Grade Level Formula, Coleman-Liau Index, SMOG (Simple Measure of Gobbledygook) Index, Linsear Write Formula, and FORCAST Readability Formula (Multimedia Appendix 2) [8]. Two-tailed t tests and an ANOVA were used for comparison.
JMIR Dermatol 2025;8:e59054
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As prior data regarding accuracy have been promising, an emerging focus has been on investigating the readability of the model’s output.
Prior studies have shown Chat GPT provides accurate and comprehensive responses to questions related to heart failure, and another demonstrated its responses were at a college reading level, highlighting the need for further assessment of the readability of GPT’s outputs [12,18].
JMIR Cardio 2025;9:e68817
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The AI-generated consent forms were directly compared with the original version for readability and content quality.
To assess the impact of LLM-based simplification, we conducted readability and structural comparisons between the original (before) and AI-edited consent (after) forms. Readability was quantitatively measured using KRead and Natmal—the 2 established readability indices for the Korean language [10,11].
J Med Internet Res 2025;27:e73222
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While improvements in readability scores have been made over time [17] and human intervention can assist in improving readability [19], challenges remain in bringing readability to an appropriate level on a mass scale.
Large language models (LLMs) can consume massive amounts of data and perform a wide array of tasks that have made them useful tools to understand and generate text [26], making them an enticing option to improve the readability of PEMs.
J Med Internet Res 2025;27:e69955
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To evaluate readability, responses from both versions were assessed using a web-based Flesch Reading Ease Score (FRES) calculator. This calculator determined the responses’ readability using two different indices: the FRES and the Flesch-Kincaid Grade Level (FKGL). These readability tests use mathematical formulas that consider factors such as sentence length and word count.
JMIR Cancer 2025;11:e63677
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However, previous research has shown that web-based educational resources for cardiovascular disease risk management do not provide appropriate information or meet the usability or readability needs of adults [18,25-29], and co-design involving end users (such as community members and medical professionals) is an underused method during resource development [24].
JMIR Infodemiology 2025;5:e55248
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