Zhiyuan and Ant Afu Deploy Lingxi X2 Triage Robot at Peking University People's Hospital

At the triage service desk of Peking University People's Hospital, Zhiyuan's Lingxi X2 is answering patient questions such as which department to register for abdominal pain, how to reach gastroenterology, and whether a specific doctor has clinic hours today. The robot listens to each query while rapidly searching its knowledge base and responding in an orderly manner.

As a hospital intelligent triage project jointly deployed by Zhiyuan and Ant Afu, Lingxi X2 serves as a "silicon receptionist" in outpatient services. It extends online AI medical service capabilities into offline hospital settings, bringing embodied AI into direct patient contact. The robot is positioned to handle routine inquiries, not to replace doctors or nurses, and it operates as part of the hospital's existing service workflow.

Peking University People's Hospital is a well-known top-tier hospital with large outpatient volume, diverse patient sources, and complex medical needs. During peak hours, traditional manual triage desks face many high-frequency, repetitive questions about departments, registration, routes, and procedures, making it difficult to consistently meet demand for instant response and precise guidance.

To address these needs, Lingxi X2 connects to the AI medical assistant in the Ant Afu app, leveraging Ant's medical large model to enable intelligent triage and Q&A. The robot combines AI medical service capabilities with embodied interaction, using human-like speech and body movements to guide patients on site. This combination is intended to make digital health services more accessible to patients who may not be comfortable using only a mobile app.

It helps triage staff handle repetitive basic inquiries, reducing consultation pressure at the lobby desk and delivering a more approachable, immersive medical service experience. With Ant Afu's multimodal intelligent Q&A, Lingxi X2 can provide department guidance, visit process consultation, medical insurance policy answers, and in-hospital navigation. These functions cover many of the most common questions raised at the front desk.

By connecting to the hospital's private knowledge base, the robot outputs standardized medical responses based on hospital information. This helps patients obtain the information they need while sharing the burden of basic consultation work at the triage desk. The private knowledge base also helps ensure that answers align with the hospital's own procedures and policies.

According to operational data from Peking University People's Hospital, by September 30, Lingxi X2 had worked at the outpatient navigation desk for 66 working days. It completed 46,686 interactions, averaging about 707 per day, with a single-day peak of 1,420 interactions. This continuous operation data offers a reference for the practical application of embodied intelligent robots in hospital outpatient scenarios.

As an offline physical intelligent service terminal, Lingxi X2 combines mobile interaction, information display, and human-like companionship. Compared with fixed screens and self-service kiosks, a half-size humanoid robot can communicate with patients through voice and gestures. This provides a more natural and friendly interaction for medical inquiries and integrates AI services into the outpatient environment. The form factor is also intended to draw attention and encourage patients to ask for help.

The deployment of this intelligent triage project is an important practice by Zhiyuan and Ant Afu in exploring smart medical services. It also pushes embodied intelligent robots from technical demonstrations toward real-world applications, making them new digital helpers that support public service capacity at the outpatient frontline. The project shows how robots can take on routine, high-frequency tasks while human staff focus on more complex patient needs.

With deeper application and broader deployment, the project will further accumulate interaction and operational experience in hospital scenarios. It aims to explore a replicable, scalable smart medical service model and provide a demonstration path for AI service upgrades at large top-tier hospitals. The collaboration also points to a potential template for combining large-model medical AI with embodied robot deployment in public healthcare settings.