Regular follow-up imaging is a cornerstone of oncologic care, enabling the monitoring of disease progression and treatment response. In radiology, this process involves the structured acquisition of comparable imaging studies over time - these individual exams are complete in their meaning based on their temporal relationship to one another. This session explores how Al already helps radiologists by enhancing and optimising oncologic follow-up imaging and how it can facilitate a more holistic assessment of oncological patients.
Chairperson's introduction
The groundwork: segmentation and lesion detection
The next steps: lesion characterisation - typification
The future: evolution of oncologic follow-up imaging assessments
Panel discussion: When will AI automate manual work in oncologic imaging?