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
Concerns Raised Over Unverified Datasets in AI Health Prediction Models
A new study finds widely used AI health prediction models are built on datasets with unverifiable origins, raising safety and validity concerns.
•EurekAlert
AI Unlocks Hidden Brain Lesions in Multiple Sclerosis via MRI
AI-enabled image processing reveals invisible cortical lesions in MRI scans of MS patients, advancing diagnosis and research.
•EurekAlert
AI-Driven Molecular Profiling Enhances Thrombosis Risk Prediction
Researchers create an AI model integrating genetic, clinical, and transcriptomic data to identify individuals at risk for thrombosis.