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

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

Abstract read

In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Shuhang Luo *Department of Urology, China-Japan Friendship Hospital, Beijing, Beijing, China.ORCID http://orcid.org/0000-0002-9992-0530

Runhua Tang *Department of Urology, Beijing Hospital, Beijing, China, 8613911036970.ORCID http://orcid.org/0009-0004-0398-7469

Ziyin Chen *Department of Urology, Beijing Hospital, Beijing, China, 8613911036970.ORCID http://orcid.org/0000-0003-4588-4076

Jianye WangDepartment of Urology, Beijing Hospital, Beijing, China, 8613911036970.ORCID http://orcid.org/0000-0003-4667-9365

Ming Liu *Department of Urology, Beijing Hospital, Beijing, China, 8613911036970.ORCID http://orcid.org/0000-0002-4770-4449

Jianfeng Wang *Department of Urology, China-Japan Friendship Hospital, Beijing, Beijing, China.ORCID http://orcid.org/0009-0007-7786-2716

Li Ma *Department of Intensive Care Medicine, The Second Hospital & The Second Clinical Medical College of Lanzhou University, No. 82 Cuiyingmen, Lanzhou, Gansu, 73003, China, 86 13893679654.ORCID http://orcid.org/0000-0002-5665-384X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: 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.

Indexed as

Artificial IntelligenceBiomedical ResearchImage Processing, Computer-AssistedAnimalsGenerative Artificial IntelligenceHumansAI-generated image detectionbiomedical image manipulationconclusion-oriented falsificationGenAIgenerative artificial intelligenceGPT Images 2.0research integrity

Identifiers

PMID42826371

DOI10.2196/100710

PMCPMC13633064

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.