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Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study.

Wenxiong Liao1, Zhengliang Liu2, Haixing Dai2

  • 1School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.

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|December 28, 2023
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Summary

Large language models like ChatGPT generate human-like text, but medical content requires validation. Machine learning models can effectively detect AI-generated medical texts, ensuring trustworthy AI use in healthcare.

Keywords:
ChatGPTartificial intelligencelinguistic analysismachine learningmedical ethicsmedical textstext classification

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Medical Informatics

Background:

  • Large language models (LLMs) like ChatGPT can produce human-like text.
  • The proliferation of AI-generated content necessitates validation, especially in medicine.
  • Erroneous medical AI content poses risks of disinformation and public harm.

Purpose of the Study:

  • Analyze linguistic differences between human-expert and ChatGPT-generated medical texts.
  • Develop machine learning (ML) workflows for detecting AI-generated medical content.
  • Promote responsible AI implementation in the medical field.

Main Methods:

  • Compiled datasets of human-authored and ChatGPT-generated medical texts.
  • Analyzed linguistic features including vocabulary, parts-of-speech, sentiment, and perplexity.
  • Designed and implemented ML models, including a transformer-based approach, for text classification.

Main Results:

  • Human medical texts were more concrete and information-rich; ChatGPT texts prioritized fluency and general terminology.
  • A transformer-based model achieved over 95% F1 score in detecting ChatGPT-generated medical text.
  • Published datasets and code facilitate further research and development.

Conclusions:

  • Linguistic characteristics of AI-generated medical text differ from human expert writing.
  • Proposed ML algorithms can reliably detect ChatGPT-generated medical content.
  • This research paves the way for accountable and trustworthy AI applications in medicine.