# FollowUpBot: An LLM-Based Conversational Robot for Automatic Postoperative Follow-up

arXiv: https://arxiv.org/abs/2507.15502 · HTML: https://mlsanity.com/paper/2507.15502/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 2 of 2)

- [EdgeShard: Efficient LLM Inference via Collaborative Edge Computing](https://mlsanity.com/paper/2405.14371/) (2024)
- [An Analysis of the Cloud Computing Security Problem](https://mlsanity.com/paper/1609.01107/) (2016)

## Built on by (most cited first, 2 of 2)

- [A Framework for Longitudinal Health AI Agents](https://mlsanity.com/paper/2604.12019/) (2026)
- [Evaluating the Challenges of LLMs in Real-world Medical Follow-up: A Comparative Study and An Optimized Framework](https://mlsanity.com/paper/2512.18999/) (2025)

## Abstract (arXiv)

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.