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
AI Analyzes 66,000 MRI Scans to Map Body Composition Risks
Researchers used AI to analyze over 66,000 whole-body MRI scans, creating a detailed body composition reference map linked to health risks.
•EurekAlert
Brain-Inspired Training Enhances AI Reliability and Uncertainty Recognition
KAIST researchers developed a brain-inspired AI training method that reduces overconfidence and improves the recognition of unfamiliar data.
•EurekAlert
AI and PS-OCT Enhance Early Keratoconus Detection
AI combined with polarization-sensitive OCT enables earlier and more accurate detection of subclinical keratoconus compared to standard tomography.