ArticleScientific reports2024
Experts fail to reliably detect AI-generated histological data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A systematic map of generative AI guidelines and reporting in ecology and evolutionary biology: towards the framework of AI disclosure for Improved Transparency (AIdIT).Research integrity and peer review · 2026Review
- Is this real? Susceptibility to deepfakes in machines and humans.Cognitive research: principles and implications · 2026Article
- Image fraud in nuclear medicine research.European journal of nuclear medicine and molecular imaging · 2026Article
- AI-generated medical realism and hantavirus misinformation: a new public health threat?Frontiers in digital health · 2026Article
- How good are medical students and researchers in detecting duplications in digital images from research articles: a cross-sectional survey.Research integrity and peer review · 2025Article
- Fake AI images will cause headaches for journals.Nature · 2025Article
- AI detectors are poor western blot classifiers: a study of accuracy and predictive values.PeerJ · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
AI-based methods to generate images have seen unprecedented advances in recent years challenging both image forensic and human perceptual capabilities. Accordingly, these methods are expected to play an increasingly important role in the fraudulent fabrication of data. This includes images with complicated intrinsic structures such as histological tissue samples, which are harder to forge manually. Here, we use stable diffusion, one of the most recent generative algorithms, to create such a set of artificial histological samples. In a large study with over 800 participants, we study the ability of human subjects to discriminate between these artificial and genuine histological images. Although they perform better than naive participants, we find that even experts fail to reliably identify fabricated data. While participant performance depends on the amount of training data used, even low quantities are sufficient to create convincing images, necessitating methods and policies to detect fabricated data in scientific publications.
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Registered trials
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