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Updated: Jun 10, 2026

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AI text detection in dentistry: a comparative analysis across generative models.

Jacopo Villa1, Daniele Garcovich2, Luca Lombardo3

  • 1Department of Dentistry, Universidad Europea de Valencia, Paseo de La Alameda 7, Valencia, 46010, Spain.

Research Integrity and Peer Review
|June 9, 2026
PubMed
Summary
This summary is machine-generated.

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Four AI text detectors effectively screen full dentistry manuscripts, while two performed poorly. A 60% threshold offers reliable manuscript-level classification for AI-generated content detection.

Area of Science:

  • Scientific Writing
  • Artificial Intelligence
  • Biomedical Informatics

Background:

  • Journals increasingly require robust screening for AI-generated scientific text.
  • Detector performance on full-length manuscripts is not well-established.
  • This study benchmarks AI text detectors using dentistry manuscripts.

Purpose of the Study:

  • To evaluate the performance of six widely used AI text detectors on full-length dentistry manuscripts.
  • To compare detector performance across different state-of-the-art AI text generators.
  • To determine a reliable threshold for classifying AI-generated text.

Main Methods:

  • 120 manuscripts were analyzed: 30 each from GPT-4.5, GPT-4o, DeepSeek-R2, and human-written.
  • Six detectors (Aidetector, GPTZero, Copyleaks, Originality.AI, Turnitin, DetectingAI) were used.
Keywords:
AI content detectionAI-generated contentChatGPTDentistry

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  • Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), and Cohen's kappa were employed for analysis.

    • Main Results:

    • Four detectors demonstrated high discrimination, outperforming Turnitin and DetectingAI.
    • A 60% threshold achieved high sensitivity and specificity with no false positives on human texts.
    • DeepSeek-R2 generated texts were most easily detected; agreement was highest among top-performing detectors.

    Conclusions:

    • Four AI text detectors show high discrimination for full dentistry manuscripts.
    • Turnitin exhibited moderate performance, while DetectingAI was ineffective.
    • A 60% threshold provides reproducible, manuscript-level classification of AI-generated text.