How Medical AI Imaging Could Expose Your Health Data: What to Know

Artificial intelligence is becoming common in medical imaging — it helps radiologists spot tumors, fractures, and other abnormalities faster than ever. But as AI adoption grows, so do concerns about how your medical images and the data attached to them are handled. A recent report from the Radiological Society of North America (RSNA) highlights that the privacy risks associated with AI in medical imaging are more serious than many patients realize.

What happened

The RSNA report, published in May 2026, warns that medical imaging AI “opens a Pandora’s box of privacy-related risks.” This includes familiar threats like data breaches and unauthorized sharing of images, but also newer problems: the ability to create deepfake medical images — realistic-looking X-rays, CT scans, or MRIs that do not correspond to a real patient.

This is not just theoretical. In March 2026, a separate RSNA study demonstrated that deepfake X-rays could fool both trained radiologists and AI diagnostic tools. Researchers created synthetic chest X-rays that appeared normal but actually contained subtle indicators of disease — or conversely, fake abnormalities that masked real health issues. When shown these images, human readers and AI models alike made incorrect diagnoses.

The technology to generate these deepfakes is improving fast, and the data needed to train such models can be sourced from real patient scans if those scans are not properly secured or anonymized.

Why it matters

Your medical images are among the most personal data you have. Unlike a credit card number, you cannot change your bone structure or lung patterns. If an X-ray or MRI is leaked, it could be used to identify you, combine with other data, or even be altered to create fraudulent records.

The more data AI systems require to improve, the more hospitals and imaging centers compile large datasets of patient scans. These datasets become attractive targets for hackers. And if your images are used to train third-party AI models, you may not have control over where that data ends up — or how it is protected downstream. Many consent forms are vague about whether de-identified data may be sold or shared for research and commercial purposes.

Deepfake medical images also introduce a new class of risk. A manipulated image inserted into a patient’s record could lead to wrong diagnosis, unnecessary treatment, or insurance fraud. While no widespread incidents have been reported yet, the RSNA research shows the capability exists.

What readers can do

You do not need to become a privacy expert, but a few practical steps can help you protect your health data:

  • Ask before you scan. When your doctor orders an X-ray, MRI, or CT, ask the imaging facility whether your images and associated data will be used to train AI models. Some facilities offer an opt-out.

  • Understand your consent form. Look for language about data sharing, de-identification, and commercial use. If it is unclear, ask for clarification — or ask to have your data excluded from any research or AI training.

  • Check for privacy protections. Inquire whether the facility uses encryption for storing and transmitting images, and what their breach notification policy is. You have a right to know how your data is protected.

  • Use patient portals securely. If you access your images through an online portal, choose a strong password and enable two-factor authentication if available.

  • Follow the news. The landscape is changing quickly. Keep an eye on updates from reputable medical and privacy organizations about AI and data security.

Sources

Radiological Society of North America (RSNA). “Medical Imaging AI Opens a Pandora’s Box of Privacy-Related Risks.” Published May 20, 2026.

Radiological Society of North America (RSNA). “Deepfake X-Rays Fool Radiologists and AI.” Published March 24, 2026.

These reports are available through the RSNA newsroom and peer-reviewed journals.