FollowUpBot: An LLM-Based Conversational Robot for Automatic Postoperative Follow-up
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Computer Science > Human-Computer Interaction
arXiv:2507.15502 (cs)
[Submitted on 21 Jul 2025]
Title:FollowUpBot: An LLM-Based Conversational Robot for Automatic Postoperative Follow-up
Authors:Chen Chen, Jianing Yin, Jiannong Cao, Zhiyuan Wen, Mingjin Zhang, Weixun Gao, Xiang Wang, Haihua Shu
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Abstract: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 this https URL.
| Subjects: | Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Robotics (cs.RO) |
| Cite as: | arXiv:2507.15502 [cs.HC] |
| (or arXiv:2507.15502v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2507.15502
arXiv-issued DOI via DataCite
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