Charles Edward Kahn, Philadelphia / United States Kicky Gerhilde Van Leeuwen, De Bilt / Netherlands
12 min
Image segmentation and beyond
René Hosch, Essen / Germany
To learn to frame clinical questions as segmentation tasks and build a lean, generalizable pipeline that turns routine scans into high-value, interoperable outputs.
To appreciate how masks become an informational gain: extract body-composition metrics, track tumor volume, and generate a clinically relevant information.
To understand seamless clinical handoff: store segmentations as DICOM-SEG, publish key values as FHIR Observations, and surface them in a linked FHIR dashboard.
12 min
Image optimisation
Christoph Hoeschen, Magdeburg / Germany
To learn about different options to improve image quality using AI based approaches.
To appreciate the high potential for reducing e.g. scan time in MR or radiation dose in CT by AI methods.
To understand critical aspects and pitfalls of the use of AI in image optimisation.
12 min
LLM and structured reporting
Moritz Christian Halfmann, Mainz / Germany
To learn how large language models (LLMs) can be applied to radiological reporting, including support in draft generation, error reduction, and workflow support.
To appreciate how LLMs can facilitate structured reporting and standardization, improving clarity, consistency, and data reusability in radiology.
To understand the current opportunities and future directions for LLM-enhanced radiology reporting, with emphasis on validation, human–AI collaboration, and clinical impact.
12 min
AI agents in radiology
Markus Wenzel, Bremen / Germany
To understand the distinguishing features of AI Agents and Multi-Agent Systems as opposed to AI Algorithms.
To appreciate the characteristics of tasks that are amenable to and benefit from AI Agents.
To learn about barriers and caveats associated with AI Agents and their technology.
10 min
Panel discussion: AI advancement expectations for 2027