The Rise of Deepfake Medical Imaging: Radiologists’ Diagnostic Accuracy in Detecting ChatGPT-generated Radiographs
Top Cited Papers
- 1 March 2026
- journal article
- Published by Radiological Society of North America (RSNA) in Radiology
- Vol. 318 (3) , e252094
Abstract
Synthetic radiographs, including OpenAI’s ChatGPT-generated ones, are not easily distinguishable from authentic radiographs by either radiologists or large language models; physician training and dedicated tools are crucial to mitigate risks.Keywords
This publication has 21 references indexed in Scilit:
- Medical large language models are vulnerable to data-poisoning attacksNature Medicine, 2025
- A vision–language foundation model for the generation of realistic chest X-ray imagesNature Biomedical Engineering, 2024
- A high-quality dataset featuring classified and annotated cervical spine X-ray atlasScientific Data, 2024
- Synthetically enhanced: unveiling synthetic data's potential in medical imaging researchEBioMedicine, 2024
- FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal RadiographsScientific Data, 2023
- Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial networkScientific Reports, 2023
- DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classificationScientific Reports, 2022
- A new generative adversarial network for medical images super resolutionScientific Reports, 2022
- Fragile Watermark in Medical Image Based on Prime Number Distribution TheoryJournal of Digital Imaging, 2021
- Watermarking Techniques used in Medical Images: a SurveyJournal of Digital Imaging, 2014