AI applications to support patients and clinicians
Some of the work focused on communication and education. Mr. Chun Wai Lee (Kowloon/HK) presented an interactive AI chatbot developed to support patient and caregiver education in paediatric interventional radiology. The tool aims to make information about procedures more accessible and easier to understand, showing how AI could support communication and education beyond the clinical team itself.
Other presentations looked at how AI might support clinicians more directly, and large language models (LLMs), such as those developed by OpenAI and Google, were a recurring topic of discussion. Dr. Umit Mustak (Sakarya/TR) presented research on LLMs’ ability to apply standardized assessment criteria for thyroid nodule biopsy. The team wanted to assess whether the models could reliably support clinicians, and the findings so far are encouraging.
“The good thing about LLMs is that they really stick to the prompt, and physicians can incorporate their own experience and their own guidelines into the prompt easily,” he said. “I want to thank the SPHAIRE organizers and CIRSE for providing the opportunity to present,” he added. “It was great to get feedback on the project.”
AI is also being explored for diagnosis and patient selection. Dr. Chiara Zerbato (Padova/IT) presented a retrospective study on whether a deep learning model could help distinguish benign from clinically significant type II endoleaks after EVAR using preoperative 3D CTA imaging. By extracting latent imaging biomarkers from the CTA volumes, the model could support more targeted patient selection and help avoid overtreatment.