用大模型驱动的机器人自动完成术后随访，又快又安全。
FollowUpBot: An LLM-Based Conversational Robot for Automatic Postoperative Follow-up
- 基于大模型的动态对话系统，支持多模式交互
- 随访覆盖率达95%以上，报告生成准确率超90%
- 本地部署保障隐私，适合医院智能护理场景
术后随访在监测康复进程和发现并发症方面至关重要。传统方法依赖床旁访谈与手工记录，耗时且人力成本高。现有数字方案如网页问卷和智能电话虽减轻护士负担，但存在交互僵化或隐私泄露风险。本文提出FollowUpBot，一种基于大模型的边缘部署机器人，可动态规划最优路径，通过多种交互模式与患者进行自适应、面对面的随访对话，确保数据隐私。同时，该系统能自动分析患者互动内容，生成结构化术后随访报告。实验表明，机器人在不同随访场景下均实现高覆盖率与满意度，报告生成准确率超过90%。演示视频见：https://www.youtube.com/watch?v=_uFgDO7NoK0。
原文摘要 · Abstract (English)
Postoperative follow-up plays a crucial role in monitoring recovery and identifying complications. However, traditional approaches, typically involving bedside interviews and manual documentation, are time-consuming and labor-intensive. Although existing digital solutions, such as web questionnaires and intelligent automated calls, can alleviate the workload of nurses to a certain extent, they either deliver an inflexible scripted interaction or face private information leakage issues. To address these limitations, this paper introduces FollowUpBot, an LLM-powered edge-deployed robot for postoperative care and monitoring. It allows dynamic planning of optimal routes and uses edge-deployed LLMs to conduct adaptive and face-to-face conversations with patients through multiple interaction modes, ensuring data privacy. Moreover, FollowUpBot is capable of automatically generating structured postoperative follow-up reports for healthcare institutions by analyzing patient interactions during follow-up. Experimental results demonstrate that our robot achieves high coverage and satisfaction in follow-up interactions, as well as high report generation accuracy across diverse field types. The demonstration video is available at https://www.youtube.com/watch?v=_uFgDO7NoK0.
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