The Hong Kong Polytechnic University and Fudan University have established the Suanova Hong Kong-Shanghai Joint Laboratory for AI in Healthcare, combining artificial intelligence research with medicine, public health and clinical expertise.
Suanova Technology, a subsidiary of Hong Kong-listed Yeebo, is supporting the laboratory with computing infrastructure. The company said its contribution also covers data cleansing, underlying network deployment and storage-system construction.
The collaboration follows a wider push to apply AI in healthcare across Hong Kong and mainland China.
Research spans generative AI and infectious disease
The universities said the laboratory will focus on areas including generative AI, infectious disease research, medical informatics and bioinformatics. The partnership also covers postgraduate and postdoctoral training, academic exchange and commercialization of research outcomes.
Fudan brings clinical and public-health capabilities, including expertise in infectious disease, while PolyU contributes research in artificial intelligence and domain-specific models.
Professor Zhang Wenhong, head of Fudan’s Institute of Infection and Health, and Professor Yang Hongxia, Executive Director of the PolyU Academy for Artificial Intelligence, signed the university collaboration agreement.
Compute infrastructure is part of the research platform
AI healthcare projects can require sensitive data, specialized models and substantial computing resources. Suanova said it will provide more than raw compute capacity, including network and storage infrastructure needed to support the laboratory’s research workloads.
Yeebo and Suanova have framed the project around domestic computing infrastructure and the use of decentralized or domain-specific models that can be deployed by hospitals and research institutions while retaining greater control over data.
That architecture is still a research direction rather than a single deployed clinical system. Medical AI also faces validation, governance and privacy requirements before research models can be used in patient care.
Universities target translation into healthcare use
PolyU said the joint laboratory is intended to move beyond algorithm development toward applications that address medical, clinical-practice and public-health needs.
The partners expect work to include infectious-disease surveillance and early warning, AI-assisted diagnosis, antimicrobial-resistance risk identification, medical informatics and potentially drug discovery.
The value of the collaboration will ultimately depend on how effectively the teams can connect computing infrastructure and model development with clinically validated problems, datasets and workflows. For hospitals, the threshold for useful AI remains higher than research accuracy alone because systems must also fit existing care processes, data-governance rules and safety requirements.
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