Foxconn unveils Nurabot at Computex 2025, AI-powered future for nursing care
At Computex 2025 in Taipei, Nvidia CEO Jensen Huang spotlighted a standout innovation that could reshape the healthcare industry: Nurabot, a collaborative nursing robot developed by Foxconn in partnership with Kawasaki Heavy Industries. Designed to combat nurse burnout and staff shortages, projected to reach 4.5 million globally by 2030, Nurabot is a shining example of how AI and robotics can ease pressure on clinical teams.
Field-tested at Taichung Veterans General Hospital (TCVGH), Nurabot helps nurses by automating physically demanding and repetitive tasks, such as delivering medication, transporting specimens, and patrolling wards. With Nvidia's Jetson Orin edge computing platform and Holoscan sensor processing, Nurabot navigates hospital environments efficiently and safely. Virtual training via Nvidia Isaac for Healthcare and Omniverse simulations ensures the robot is well-prepared before real-world deployment.
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'In one of our wards, Nurabot delivers wound care kits and health education materials,' said Shu-Fang Liu, deputy director of nursing at TCVGH. 'It saves multiple trips, reduces fatigue, and frees us to focus more on patients.'
Foxconn's broader vision for AI-enabled smart hospitals is built on Nvidia's full-stack solution, from data centre to bedside. Using DGX systems at its Hon Hai Super AI Computing Center and the Nvidia NeMo framework, Foxconn has developed FoxBrain, a large language model capable of speech recognition and natural dialogue. It powers Nurabot and supports the CoDoctor AI platform, improving diagnostics in fields like cardiology, oncology, and ophthalmology.
Hospitals such as TCVGH, Cardinal Tien, and Baishatun Tung – Mazu Hospital are also benefiting from digital twins, created in Nvidia Omniverse, to design and optimise ward layouts and train robotic assistants in realistic 3D simulations.
TCVGH's ongoing trial of Nurabot is already seeing strong results, with plans to deploy dozens more units by year-end. Liu hopes future versions will interact in multiple languages and assist with patient mobility, allowing a single nurse to safely move a patient, where two are typically needed.
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