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Identifying ChatGPT-Written Patient Education Materials Using Text Analysis and Readability.

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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.

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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.