Paper: Agentic Systems in Radiology: Design, Applications, Evaluation and Challenges

Beyond building ever more capable AI models, much can be gained from making the models we already have work better with our systems and workflows. This is a critical step into making these models more useful for radiology, a field of highly complex, multi-step tasks.

Building artificial agents — systems that observe their environment and act on it — has long been a goal of AI. The emergence of large language models (LLMs) has recently made this pursuit much more feasible, thanks to their remarkable ability to process and interact in natural language. This enables LLMs to plan, reason, and make decisions, though how far that capability extends remains a matter of active research. Embedding LLMs within frameworks where they can iteratively refine their context and select between tools allows them to interact flexibly with connected systems.