AI-generated X-rays can now appear convincing enough to mislead even expert radiologists, according to a study published March 24 in Radiology, the journal of the Radiological Society of North America. The research found that radiologists were able to correctly identify whether X-ray images were real or synthetic only 75% of the time on average, despite knowing that fake images were included in the dataset.
The study involved 17 radiologists from 12 research centers across six countries: the United States, France, Germany, Turkey, the United Kingdom, and the United Arab Emirates. Participants reviewed 264 X-ray images, split evenly between authentic scans and AI-generated ones. Before learning the study’s true aim, only 41% of them independently suspected that some of the images may have been produced by artificial intelligence.
Lead author Mickael Tordjman, a radiologist at the Icahn School of Medicine at Mount Sinai in New York, said the findings show how far synthetic medical imaging has advanced. “Our study demonstrates that these deepfake X-rays are realistic enough to deceive radiologists, the most highly trained medical image specialists, even when they were aware that AI-generated images were present,” he said.
Performance varied widely among the radiologists, with individual scores ranging from 58% to 92%. The researchers found no meaningful relationship between detection ability and professional experience, which ranged from no experience to 40 years. Musculoskeletal radiologists performed better than other subspecialists. The team also tested four multimodal large language models, including GPT-4o and Gemini 2.5 Pro, which achieved accuracy rates between 57% and 85% when assessing ChatGPT-generated images.
The implications extend well beyond image interpretation. Tordjman said the results point to a serious vulnerability in areas such as legal disputes and hospital cybersecurity, including scenarios in which fake fractures could be used in fraudulent claims or synthetic scans could be inserted into medical systems to influence diagnoses.
Outside experts also raised concerns. Elisabeth Bik, a microbiologist and specialist in image integrity, told Nature that the findings were “both disturbing and not very surprising,” and said the risks affect research integrity, clinical workflows, insurance claims, and legal proceedings that rely on imaging evidence.
Tordjman noted that some synthetic images still contain subtle warning signs, including bones that appear overly smooth, spines that look unnaturally straight, and blood vessel patterns that seem too uniform. Still, the study suggests that visual inspection alone is no longer a reliable safeguard. The authors called for countermeasures such as invisible watermarking and cryptographic signatures applied at the moment images are captured.
The researchers also warned that the current problem may be only an early stage of a broader challenge. “We are potentially only seeing the tip of the iceberg,” Tordjman said, pointing to AI-generated 3D scans such as CT and MRI as a likely next step. He said building training datasets and detection tools now will be essential before those threats become even harder to manage.
