Scan-Do Attitude: Towards Autonomous CT Protocol Management Using a Large Language Model Agent
Kang X, Vorberg L, Maier A, Katzmann A, Taubmann O (2026)
Publication Type: Conference contribution
Publication year: 2026
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 16147 LNCS
Pages Range: 46-54
Conference Proceedings Title: Lecture Notes in Computer Science
Event location: Daejeon
ISBN: 9783032060037
DOI: 10.1007/978-3-032-06004-4_5
Abstract
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 tool-calling 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.
Authors with CRIS profile
Linda Vorberg
Lehrstuhl für Informatik 5 (Mustererkennung)
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
How to cite
APA:
Kang, X., Vorberg, L., Maier, A., Katzmann, A., & Taubmann, O. (2026). Scan-Do Attitude: Towards Autonomous CT Protocol Management Using a Large Language Model Agent. In Jianing Qiu, Jinlin Wu, Curtis Langlotz, Baoru Huang, Zhen Lei, Honghan Wu, Hongbin Liu, Weidi Xie (Eds.), Lecture Notes in Computer Science (pp. 46-54). Daejeon, KR: Springer Science and Business Media Deutschland GmbH.
MLA:
Kang, Xingjian, et al. "Scan-Do Attitude: Towards Autonomous CT Protocol Management Using a Large Language Model Agent." Proceedings of the 1st International Workshop on Agentic AI for Medicine, Agentic AI 2025, 1st International Workshop on Clinical-Driven Robotics and Embodied AI Technology, CREATE 2025, and 1st International Workshop on Multimodal Large Language Models in Clinical Practice, CMLLMs 2025, Held in Conjunction with the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, Daejeon Ed. Jianing Qiu, Jinlin Wu, Curtis Langlotz, Baoru Huang, Zhen Lei, Honghan Wu, Hongbin Liu, Weidi Xie, Springer Science and Business Media Deutschland GmbH, 2026. 46-54.
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