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Identifying ChatGPT-Written Patient Education Materials Using Text Analysis and Readability
Silas Monje1, Sophie Ulene2, Alexis C Gimovsky3
1The Warren Alpert Medical School, Brown University, Providence, Rhode Island.
American Journal of Perinatology
|April 9, 2024
Summary
Simple grammatical analysis can reliably differentiate AI-generated text from human-written medical articles. A new scoring system achieved 94.1% sensitivity and 100% specificity in distinguishing ChatGPT-generated content.
Area of Science:
- Medical Informatics
- Natural Language Processing
Background:
- Artificial intelligence (AI) text generators like ChatGPT are increasingly used in medicine.
- Differentiating AI-generated content from human-written text is crucial for medical accuracy and integrity.
- Previous research suggests grammatical analysis can distinguish AI-generated text.
Purpose of the Study:
- To develop and validate a method for identifying AI-generated obstetric articles.
- To assess the feasibility of using simple grammatical analysis for this purpose.
Main Methods:
- ChatGPT generated 25 patient education articles on obstetric topics.
- AI-generated articles were analyzed for readability and grammar using validated scoring systems.
- A novel scoring system was developed based on Flesch-Kincaid score, character count, and average word length.
- The scoring system was tested on 17 new AI-generated and 7 human-written ACOG articles.
Main Results:
- AI-generated articles had fewer characters (3,066 vs. 7,426) and lower Flesch-Kincaid scores (46 vs. 59) than human-written articles.
- The novel scoring system differentiated AI-generated from human-written articles with 94.1% sensitivity and 100% specificity (AUC 0.99).
- Average word length was greater in AI-generated articles (5.3 vs. 4.8 words).
Conclusions:
- Simple grammatical analysis, including readability scores and text characteristics, can accurately identify current AI-generated medical texts.
- As AI integration in medicine grows, tools to distinguish AI-generated content are essential for healthcare stakeholders.
- This study demonstrates a feasible and accurate method for detecting AI-generated obstetric content.
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