扫描态度：利用大语言模型智能体实现自主CT协议管理
Scan-do Attitude: Towards Autonomous CT Protocol Management using a Large Language Model Agent
AI总结：
针对CT扫描协议管理耗时且劳动力短缺的问题，提出基于大语言模型的智能体框架，通过上下文学习与工具调用实现协议配置的自动解析与执行，实验证明其可行但受限于设备API统一性。
摘要：
管理计算机断层扫描（CT）中的扫描协议，包括调整采集参数或配置重建，以及以患者特定方式选择后处理工具，既耗时又需要临床和技术专业知识。与此同时，我们观察到放射学领域熟练劳动力日益短缺。为解决这一问题，提出了一种基于大语言模型（LLM）的智能体框架，以协助解释和执行以自然语言或结构化、设备无关格式给出的协议配置请求，旨在提高工作流程效率并减少技术人员的负担。该智能体结合了上下文学习、指令遵循和结构化工具调用能力，以识别相关协议元素并应用准确的修改。在系统评估中，实验结果表明，该智能体能够有效检索协议组件，生成设备兼容的协议定义文件，并忠实地实现用户请求。尽管原则上证明了可行性，但由于缺乏统一的设备API，该方法在语法和语义有效性方面面临限制，并面临模糊或复杂请求的挑战。总之，研究结果展示了基于LLM的智能体支持CT成像中扫描协议管理的清晰路径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
