Radiologists and leading AI models struggle to distinguish AI-generated deepfake X-ray images from authentic radiographs, according to a recent Radiology journal study.
Key Details
- 1Study involved 264 X-ray images, half real and half generated by AI models, including ChatGPT-4o and RoentGen.
- 217 radiologists from six countries participated, with accuracy in identifying deepfake images ranging from 58% to 92%.
- 3Four leading multimodal LLMs (GPT-4o, GPT-5, Gemini 2.5 Pro, Llama 4 Maverick) had detection accuracies between 52% and 89%.
- 4No correlation was found between years of radiology experience and accuracy; musculoskeletal specialists performed better than others.
- 5Common AI-deepfake X-ray features included overly smooth bones, symmetric lungs, and unnaturally straight spines.
- 6Proposed safeguards include invisible watermarks and cryptographic image signatures.
Why It Matters
These findings expose high-stakes risks for clinical practice, legal proceedings, and hospital cybersecurity as synthetic images become increasingly authentic. Urgent adoption of digital safeguards and specialized training is needed to protect the integrity of medical imaging.
Source
EurekAlert
Related News
•EurekAlert
UVA's $4M NIH Grant Backs AI to Optimize Cardiac MRI Therapies
UVA secures $4 million NIH funding to develop GenStrain, an AI model using MRI data to personalize cardiac pacing therapies.
•EurekAlert
Machine Learning Models Predict Cancer Risk with Routine Health Data
Researchers developed machine learning models using routinely collected health data to predict individual risk for 20 common cancers, outperforming traditional methods for several cancer types.
•EurekAlert
Duke Receives $25M to Lead National Trustworthy AI Hub for Dementia and Aging
Duke University has launched a $25 million national center to make AI for aging and dementia clinically robust and trustworthy.