Prime Highlights :
- ACR releases a patient handout explaining AI-generated radiology report summaries and their limitations.
- The resource encourages patients to discuss unclear or concerning summary details with their provider.
Key Facts :
- ACR’s Patient- and Family-Centered Care and Artificial Intelligence Economics Committees jointly develop the two-page handout.
- A recent study finds large language models can improve patient understanding of imaging results.
Background :
The American College of Radiology has released a new resource to help patients better understand artificial intelligence-generated summaries of their imaging reports.
The college’s Patient- and Family-Centered Care Economics Committee created the handout to teach patients how to use these increasingly common report summaries more effectively.
The two-page explainer covers AI’s role in radiology, the purpose behind these summaries and how they can support conversations with caregivers. ACR said the patient-friendly handout can strengthen discussions around AI and help patients and families better understand their radiology findings, urging members to fold the resource into patient education materials and clinical workflows wherever AI-generated summaries appear.
Developed alongside the college’s Artificial Intelligence Economics Committee, the handout addresses common questions patients raise about AI in imaging, including why artificial intelligence cannot read images on its own and whether the technology faces proper regulation.
ACR stressed that AI-generated summaries do not remove a patient’s responsibility to discuss imaging results directly with their provider, noting that the technology does not review study images or offer new medical opinions within these auto-generated explainers.
The handout advises patients to speak with their provider or radiologist whenever something in a summary feels unclear, confusing or concerning, adding that AI can make medical information easier to grasp, though conversations with a healthcare team remain essential.
A recent study in the Journal of the American College of Radiology found that large language models can meaningfully boost how well patients understand their imaging results.