Teda Biotech's fiscal year 2025 revenue growth surpasses expectations, and AI medical business begins commercial validation

Ask AI · How to Build an AI Medical Computing Power Base and Data Barriers through Cooperation?

【Global Network Finance Comprehensive Report】On the evening of March 31, 2025, Teda Biotech (08189.HK), a company listed on the Hong Kong Stock Exchange, released its 2025 annual performance report, disclosing operating revenue of 523 million yuan, a year-on-year increase of 35.67%, and a net profit attributable to shareholders of -90.82 million yuan.

Of particular note is that the main reason for Teda Biotech’s significant revenue growth comes from strategic adjustments to its business structure. Its traditional fertilizer business has been gradually scaled back, while its emerging AI medical business has achieved breakthrough progress. In this regard, the company also disclosed in its announcement: “The company’s AI health and elderly care products and services recorded full-year revenue of 109 million yuan, and in the reporting period it reached breakeven, becoming the core growth engine for the company’s future development.”

Public information shows that Teda Biotech is directly facing a market landscape in which structural adjustments to traditional business are intensifying. With determination, it has pressed the accelerator for strategic transformation, targeting the high-growth AI medical and healthcare track.

In AI medical services, competition is essentially a contest between technological infrastructure and data barriers. The hardware layer is the foundation for AI medical computing power. Teda Biotech has already reached a strategic cooperation with Suihong Huachuang, a subsidiary of Hongxin Electronics, launching the “Xihe No. 1” medical large model. Through this initiative, it builds a domestically produced computing power base that is secure, controllable, and elastically expandable, laying a solid foundation for the industrialization and ongoing iteration of full-stack AI medical models.

At the same time, the company cooperates with the Shenzhen Institute of Computing Science. Through a coordinated model of “technology supply + engineering implementation,” it jointly promotes high-quality industrial applications of domestically developed foundational software in key areas such as healthcare, government affairs, and finance. The company also cooperates with Peking University Third Hospital to obtain million-level real clinical case data in a closed-source form. With data assets that are the closest to real clinical scenarios, it establishes a data moat.

In its “2025 China Medical Artificial Intelligence Market Forecast,” IDC points out: “By 2026, medical large models trained on high-quality proprietary data in vertical domains will have a clinical adoption rate 300% higher than that of general models, because what doctors need is an assistant that ‘understands pathology,’ not a chatterbox that ‘understands language.’” The “Xihe No. 1” medical large model directly targets this industry pain point.

In addition, confidence in implementing strategic transformation is also reflected in executive personnel changes. The company’s latest disclosure states that it will appoint Ms. Lynn Li—an expert who has devoted herself to the intersection of biotechnology and AI frontiers—as Co-CEO of the company. She will be fully responsible for the technological iteration, business expansion, and capital market strategy of the group’s “AI Medical Large Model MaaS platform,” particularly focusing on the continuous optimization and application deployment of the core “MaaS platform” business that she will lead.

Public information further shows that from pilot implementations in Xiamen and Yinchuan to nationwide large-scale replication, Teda Biotech’s AI medical footprint is expanding rapidly. While realizing the company’s own commercial value, it also enables grassroots medical institutions to have AI-assisted capabilities at the same level as tertiary hospitals, narrowing urban–rural healthcare disparities, reducing overall healthcare costs for society as a whole, and achieving the social value of “high-quality medical resources accessible to everyone.”