Scan-do Attitude: Towards Autonomous CT Protocol Management using a Large Language Model Agent
On the map: LLM agents and tool use → Medical and scientific domain agents
Abstract (arXiv)
Managing scan protocols in Computed Tomography (CT), which includes adjusting acquisition parameters or configuring reconstructions, as well as selecting postprocessing tools in a patient-specific manner, is time-consuming and requires clinical as well as technical expertise. At the same time, we observe an increasing shortage of skilled workforce in radiology. To address this issue, a Large Language Model (LLM)-based agent framework is proposed to assist with the interpretation and execution of protocol configuration requests given in natural language or a structured, device-independent format, aiming to improve the workflow efficiency and reduce technologists' workload. The agent combines in-context-learning, instruction-following, and structured toolcalling abilities to identify relevant protocol elements and apply accurate modifications. In a systematic evaluation, experimental results indicate that the agent can effectively retrieve protocol components, generate device compatible protocol definition files, and faithfully implement user requests. Despite demonstrating feasibility in principle, the approach faces limitations regarding syntactic and semantic validity due to lack of a unified device API, and challenges with ambiguous or complex requests. In summary, the findings show a clear path towards LLM-based agents for supporting scan protocol management in CT imaging.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 7 of its 10 references that are in the map. First posted on arXiv 24 Sep 2025.
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7 of 10 references.
References · 7
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
- 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 343
- 2022TabLLM: Few-shot Classification of Tabular Data with Large Language Modelscited by 277
- 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
- 2024EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instructioncited by 154
- 2024TableRAG: Million-Token Table Understanding with Language Modelscited by 41
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- 2026Towards Autonomous and Auditable Medical Imaging Model Development
- 2025ReX-MLE: The Autonomous Agent Benchmark for Medical Imaging Challenges
- 2026RadLite: Multi-Task LoRA Fine-Tuning of Small Language Models for CPU-Deployable Radiology AI