From AI diagnostics to exoskeletons, the event highlights how healthcare tech is moving into real-world use
Updated
April 8, 2026 10:43 AM
Tesla Bot Optimus, designed by Tesla. PHOTO: ADOBE STOCK
The China International Medical Equipment Fair 2026 will open in Shanghai from April 9 to 12 at the National Exhibition and Convention Center. It is one of the largest gatherings in the medical device industry. This year’s edition will cover more than 320,000 square metres. Nearly 5,000 companies and brands are expected to participate, representing over 20 countries and regions. Organisers also expect more than 200,000 professional visitors and buyers from around 150 markets.
A key focus this year is the growing use of artificial intelligence in healthcare. One of the headline technologies is an AI agent designed to carry out multiple diagnoses from a single scan. The exhibition will also feature diagnostic software that is already in clinical use. In addition, an integrated platform for AI training and inference will be showcased to improve computing capacity within healthcare institutions.
Robotics will also play a central role at the event. New systems across surgical procedures, rehabilitation and elderly care are expected to be presented. Together, these developments point to a steady move toward more precise and assisted forms of care. Many of these technologies are designed to support clinicians and patients, especially in tasks that require consistent accuracy or long-term physical assistance.
For the first time, the event will introduce a dedicated Future Tech Arena. It will focus on brain-computer interfaces, embodied intelligence and university-led innovation. The space will include AI-assisted MRI systems for Alzheimer’s diagnosis. It will also feature brain-computer interface technologies used for cognitive assessment and training, along with wearable robotic exoskeletons.
Alongside product showcases, the event will continue to act as a platform for international trade and collaboration. An International Zone will host exhibitors from countries such as the United States, Germany, Japan, South Korea, the United Kingdom, France, Singapore, Malaysia and Thailand. This provides a view of how different markets are approaching medical technology. It also reflects the global nature of innovation and deployment in this sector.
The programme will include a set of networking and exchange formats under its “We” initiative. These include discussion stages with representatives from consulates and industry organisations, as well as matchmaking sessions based on verified buyer demand. Guided tours will also be organised to help international visitors connect with relevant exhibitors. In parallel, organisers are working with hospital partners to provide medical support services for attendees during the event.
Across the four days, hundreds of forums are scheduled. These will bring together policymakers, researchers and industry leaders to discuss regulatory frameworks, market access and the future of healthcare innovation. Some of these sessions will be led by the Global Harmonization Working Party in collaboration with the Ministry of Health of Malaysia, with a focus on regulatory alignment and cross-border cooperation in medical devices.
As healthcare systems continue to adopt digital tools and advanced equipment, events like CMEF provide a clear view of how these technologies are being developed and applied. The scale of participation this year reflects continued activity across both innovation and international collaboration in the medical device sector.
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Robots that learn on the job: AgiBot tests reinforcement learning in real-world manufacturing.
Updated
January 8, 2026 6:34 PM
A humanoid robot works on a factory line, showcasing advanced automation in real-world production. PHOTO: AGIBOT
Shanghai-based robotics firm AgiBot has taken a major step toward bringing artificial intelligence into real manufacturing. The company announced that its Real-World Reinforcement Learning (RW-RL) system has been successfully deployed on a pilot production line run in partnership with Longcheer Technology. It marks one of the first real applications of reinforcement learning in industrial robotics.
The project represents a key shift in factory automation. For years, precision manufacturing has relied on rigid setups: robots that need custom fixtures, intricate programming and long calibration cycles. Even newer systems combining vision and force control often struggle with slow deployment and complex maintenance. AgiBot’s system aims to change that by letting robots learn and adapt on the job, reducing the need for extensive tuning or manual reconfiguration.
The RW-RL setup allows a robot to pick up new tasks within minutes rather than weeks. Once trained, the system can automatically adjust to variations, such as changes in part placement or size tolerance, maintaining steady performance throughout long operations. When production lines switch models or products, only minor hardware tweaks are needed. This flexibility could significantly cut downtime and setup costs in industries where rapid product turnover is common.
The system’s main strengths lie in faster deployment, high adaptability and easier reconfiguration. In practice, robots can be retrained quickly for new tasks without needing new fixtures or tools — a long-standing obstacle in consumer electronics production. The platform also works reliably across different factory layouts, showing potential for broader use in complex or varied manufacturing environments.
Beyond its technical claims, the milestone demonstrates a deeper convergence between algorithmic intelligence and mechanical motion.Instead of being tested only in the lab, AgiBot’s system was tried in real factory settings, showing it can perform reliably outside research conditions.
This progress builds on years of reinforcement learning research, which has gradually pushed AI toward greater stability and real-world usability. AgiBot’s Chief Scientist Dr. Jianlan Luo and his team have been at the forefront of that effort, refining algorithms capable of reliable performance on physical machines. Their work now underpins a production-ready platform that blends adaptive learning with precision motion control — turning what was once a research goal into a working industrial solution.
Looking forward, the two companies plan to extend the approach to other manufacturing areas, including consumer electronics and automotive components. They also aim to develop modular robot systems that can integrate smoothly with existing production setups.