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 Pathology Tool SÉMIL Improves Stage II Bowel Cancer Risk Assessment
A La Trobe University-developed AI tool accurately predicts relapse risk in stage II bowel cancer using digital pathology images and descriptions.
•EurekAlert
AI Tool Predicts Which Rectal Cancer Patients Benefit from Intensive Therapy
UCL researchers developed an AI that analyzes biopsy slides to identify rectal cancer patients who benefit from adding irinotecan to standard chemoradiotherapy.
•EurekAlert
AI-Guided Handheld Cardiac Ultrasound Reduces Referrals and Costs in Spain
AI-guided handheld cardiac ultrasound enables primary care physicians to detect heart failure, reducing specialist referrals and saving costs.