NUS and PolyU Partner on AI-Powered Knee Osteoarthritis Detection

The National University of Singapore's Yong Loo Lin School of Medicine and the Hong Kong Polytechnic University have formalised a research partnership to deploy AI-powered tools for early detection and management of knee osteoarthritis in community settings. The Memorandum of Understanding, signed on June 5, establishes the JointCare programme — an initiative that combines AI-enabled motion profiling with structured health assessments to identify at-risk individuals outside traditional hospital environments.

Knee osteoarthritis affects a significant and growing portion of Singapore's ageing population, yet most current detection relies on patients presenting at hospitals after symptoms have already progressed. The JointCare programme takes a different approach: it uses a label-free, AI-driven motion profiling system to assess lower-limb movement patterns associated with the condition, paired with a standardised knee health questionnaire. The combined assessment allows clinicians to distinguish between patients who can be managed through conservative community care and those who need earlier specialist intervention.

The programme is structured in three phases. A pilot in Boon Lay during Q4 2026 will serve as the proof-of-concept, after which the model will roll out across communities in western Singapore. Phase two expands the collaboration into an international multi-centre study involving Hong Kong and potentially other countries. Beyond the clinical application, the MOU also creates education and training pathways for clinician-scientists, researchers, and students to work on AI-enabled healthcare delivery and population health research at scale.

This partnership is significant because it takes AI out of the hospital imaging department and into the community centre, where the majority of Singaporeans receive their primary care. The motion profiling technology does not require expensive MRI machines or specialist radiologists — it works with standard cameras in a primary care setting, making it scalable for Singapore's network of polyclinics and community care facilities. For an academic collaboration, that kind of real-world deployment focus is relatively rare.

Why it matters for Singapore: With one of the world's fastest-ageing populations and a healthcare system already under pressure from chronic disease management, Singapore needs scalable, non-hospital-based solutions for musculoskeletal conditions. AI-enabled tools that can triage patients at the community level before they need specialist referral reduce downstream burden on the hospital system while catching conditions earlier. The NUS-PolyU collaboration is a practical example of how AI research can translate directly into primary healthcare delivery, and the phased rollout gives Singapore a template for community-based AI deployment that other Southeast Asian health systems could follow.