Biomedical Research Images Manipulated by Generative AI to Alter Scientific Outcomes: Diagnostic Study of Human and Automated Detection.

Authors

Shuhang Luo, Runhua Tang, Ziyin Chen, Jianye Wang, Ming Liu, Jianfeng Wang, Li Ma

Published in

Journal of medical Internet research. Volume 28. Pages e100710. Oct 02, 2026. Epub Oct 02, 2026.

Abstract

In a diagnostic study of 104 western blot and subcutaneous xenograft tumor images, a high-fidelity generative model produced forgeries that could alter the conclusions of a study; 24 PhD-level expert reviewers could not reliably distinguish the forgeries from authentic figures (mean accuracy 50.5%, SD 6.9%), while the best-performing commercial AI detector achieved only moderate discrimination (area under the curve 0.790, 95% CI 0.695-0.885), revealing critical vulnerabilities in current research-integrity safeguards.

PMID:
42826371
Bibliographic data and abstract were imported from PubMed on 03 Oct 2026.

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