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Experts fail to reliably detect AI-generated histological data.

Jan Hartung1,2,3,4, Stefanie Reuter5, Vera Anna Kulow6

  • 1Institute for Physiology, Faculty of Medicine, University of Freiburg, 79108, Freiburg, Germany. jan.hartung@physiologie.uni-freiburg.de.

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|November 20, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence can now generate realistic histological images, fooling even scientific experts. This breakthrough necessitates new methods and policies to detect AI-generated data in scientific publications.

Keywords:
Artificial intelligenceFraudHistologyMisconductPublishingStable diffusion

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Area of Science:

  • Digital pathology
  • Artificial intelligence
  • Image forensics

Background:

  • AI image generation is rapidly advancing, posing challenges to image authenticity.
  • AI-generated images can mimic complex structures like histological samples, raising concerns about data fabrication.

Purpose of the Study:

  • To assess human and expert ability to distinguish AI-generated histological images from real ones.
  • To evaluate the effectiveness of Stable Diffusion in creating convincing artificial histological samples.

Main Methods:

  • Utilized Stable Diffusion, a recent generative algorithm, to create artificial histological samples.
  • Conducted a study with over 800 participants to test discrimination between real and AI-generated images.
  • Analyzed participant performance based on training data quantity.

Main Results:

  • Even experts could not reliably differentiate between genuine and AI-generated histological images.
  • Participant performance improved with training but remained imperfect.
  • Convincing artificial images can be generated with even small amounts of training data.

Conclusions:

  • Current AI image generation techniques can produce highly deceptive histological images.
  • Existing human perceptual capabilities are insufficient to reliably detect AI-fabricated scientific data.
  • Urgent development of detection methods and policy changes is required to ensure data integrity in scientific publications.