How AI in Medical Imaging Could Expose Your Private Health Data—and What You Can Do

Artificial intelligence is being rolled into radiology departments at a rapid clip. AI tools can spot tumors, fractures, and anomalies in CT scans and MRIs faster than a human eye, which sounds like good news for patients. And it often is. But the same technology that boosts diagnostic accuracy also creates new routes for your private health data to be accessed, shared, or used without your knowledge. A recent article from the Radiological Society of North America (RSNA) published May 20, 2026, lays out the privacy risks that come with these tools and warns that the industry may be moving faster than its safeguards. Here is what you need to know and what steps you can take.

What happened

The RSNA article highlights that medical images are not just pictures of your anatomy. They contain rich biometric data—facial structure, unique bone shapes, even patterns of blood vessels—that can be used to identify a person even after names and IDs are stripped away. This is called re-identification. When AI systems are trained on large datasets of medical images, there is a risk that patient data is used without explicit consent. Some AI models are developed by third-party companies that receive anonymized or de-identified images, but de-identification is not always irreversible. The article points out that as AI is more deeply integrated into imaging workflows, the ways data flows between hospitals, cloud servers, and AI vendors multiply, making it harder to track where your images end up.

Why it matters

For an everyday patient, this means your chest X-ray or brain MRI could be used to train a commercial AI model that you never agreed to. Even if your name is removed, researchers have demonstrated that faces reconstructed from CT scans can be matched to public photos. Insurance companies, employers, or other parties might infer health conditions from image metadata. The RSNA article notes that current regulations—including HIPAA in the United States—were not written with AI training pipelines in mind. Gaps exist in how consent is obtained, how data is de-identified, and how patients can opt out. The uncertainty around enforcement and auditing means that many imaging departments do not have clear policies yet.

What readers can do

Patients are not powerless. You have a right to ask questions and make informed decisions before you undergo an imaging exam. Here are concrete steps you can take:

  1. Ask before the scan. When your doctor orders an X-ray, CT, or MRI, ask the scheduling office or radiology department: “Will AI be used to analyze my images? Will my images be shared with any third party for AI training?” Some hospitals have already started informing patients; others will have to if enough people ask.

  2. Request an opt-out option. If the facility uses AI tools that involve external vendors, ask if you can have your images excluded from any research or training datasets. Not all institutions have a formal process yet, but raising the question puts pressure on them to create one.

  3. Use patient portals carefully. When you access your images through a patient portal, be aware that any third-party apps you connect to that portal (e.g., for storing or sharing images) may not have strong privacy protections. Keep your portal password strong and avoid sharing your medical images on social media or unsecured platforms.

  4. Read consent forms carefully. Before signing a general consent for treatment or for “use of data for research,” read what it says about AI and data sharing. If it is vague, ask for clarification. You have the right to decline non-essential data uses without affecting your care.

  5. Follow developments in your state. Some states are passing laws that require transparency around AI use in healthcare. Check whether your state’s attorney general or health department has issued guidance. Knowing your local rules empowers you to hold providers accountable.

Sources

  • RSNA Article: “Medical Imaging AI Opens a Pandora’s Box of Privacy-Related Risks” (May 20, 2026)
  • Fact notes from published research on re-identification risks from medical imaging data
  • General knowledge of HIPAA limitations regarding AI training datasets

If you are scheduled for an imaging exam in the near future, take a few minutes to ask these questions. Your images are yours, and you should have a say in how they are used—even when AI is making the diagnosis faster.