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FollowUpBot represents a significant application of Large Language Models in healthcare, specifically leveraging edge deployment to enable adaptive, privacy-preserving patient interactions during postoperative recovery. By replacing rigid scripted systems with dynamic conversational capabilities and automated report generation, this research addresses critical bottlenecks in nursing workload while mitigating data leakage risks associated with cloud-based alternatives. The study validates the potential of LLM-driven robotics to enhance both clinical coverage and user satisfaction through multimodal engagement strategies.
FollowUpBot: An LLM-Based Conversational Robot for Automatic Postoperative Follow-up
arXiv:2507.15502v1 Announce Type: cross 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 https://www.youtube.com/watch?v=_uFgDO7NoK0.
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.
Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Robotics (cs.RO)
arXiv:2507.15502 [cs.HC]
(or arXiv:2507.15502v1 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2507.15502 Focus to learn more arXiv-issued DOI via DataCite
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