How the “Yujian Cup” AI Competition is Shaping the Future of Medical Testing in China

On August 24, the first developer competition for the Chinese third‑party medical testing industry, the "Yujian Cup" Medical Testing AI Developer Competition, was held in Guangzhou under the guidance of the Guangzhou Science and Technology Bureau and jointly organized by Jinyu Medical and Huawei Cloud.

From 405 teams worldwide, ten finalists presented intense on‑site defenses, and the "Rainbow Children" team won the championship, receiving a 100,000 CNY prize.

Key officials—including Guangzhou Vice‑Chairman of the CPPCC and Director of the Science and Technology Bureau Wang Guìlín, Huawei Cloud Vice President Huang Jin, and senior scientists from Jinyu Medical—delivered speeches highlighting the event as a model for cross‑domain AI talent cultivation and the integration of biomedical data with next‑generation technologies.

Participants represented leading universities, biomedical data startups, medical institutions, and research units, submitting over 5,000 high‑quality works. The competition emphasized AI techniques such as advanced algorithm design, data preprocessing, model decision‑making, and domain‑specific annotation, with many teams employing state‑of‑the‑art academic algorithms.

Notable insights included:

Real‑world cervical cytology images with professional pathology annotations provided a rare dataset for AI development.

Teams applied cutting‑edge AI models, performed image augmentation, size adjustments, and model fusion to improve classification accuracy.

Cross‑disciplinary expertise—combining computer science and biomedical knowledge—enhanced algorithm precision and highlighted the potential of AI in medical diagnostics.

The event also featured expert talks on smart pathology and big‑data operating systems, underscoring the broader vision of a smart medical ecosystem.

Organizers plan to hold the competition annually, with future editions focusing on medical text processing, natural language processing, and knowledge graph research to further advance AI applications in medical testing.