Why China’s real-world testbeds hold the key to scaling global healthcare innovation

Healthcare systems around the world are facing a growing paradox: demand continues to rise, while capacity struggles to keep pace. Clinicians have less time for patients, costs are escalating and sustainability pressures are intensifying. The question is no longer whether innovation can help address these challenges; it is how quickly we can identify what works and scale it effectively.

To do so, we need more than isolated pilots. We need a real‑world testbed where industry, clinicians and policy-makers can rapidly test solutions, generate measurable outcomes and scale innovations in real operating conditions.

From my perspective leading Philips in Greater China, few markets are better positioned to enable this transformation. China’s scale, complexity and pace of digital adaption make it a unique environment for testing and scaling healthcare innovation. With roughly 1.4 billion people generating millions of healthcare visits every day, an ageing population of over 320 million people aged 60+, and chronic diseases accounting for over 80% of deaths, China continuously puts its healthcare system and innovation to the test.

At the same time, China has emerged as the world’s second-largest medtech market, supported by strong digital infrastructure and policy momentum. Its AI healthcare market is expected to exceed RMB 100 billion ($14.7 billion) by 2030. In the meantime, national targets are advancing the deployment of AI-enabled primary care support and medical imaging in hospitals. By connecting policy, research, clinical and industry practice, China can move proven innovations from pilot to scale faster than almost anywhere else.

Across many healthcare environments, a persistent challenge remains: teams are stretched, yet patient experience and outcomes improve only marginally. Clinicians spend valuable time navigating fragmented systems; technicians manage manual processes; workflows are inconsistent and often duplicated. These inefficiencies lead to exhausted staff, variability in care and missed opportunities for early interventions. While digital innovation has made progress, there is a clear need to do more, and to do it differently.

Philips’ Future Health Index reports published in recent years highlighted the measurable impact of AI in healthcare: 71% of healthcare professionals globally report improved workflow efficiency through AI, while 84% of healthcare professionals in China see AI as critical to enabling earlier intervention. These findings demonstrate that when AI is thoughtfully integrated into clinical workflows, it can help healthcare teams work more efficiently, expand access to care and create more time for higher-value patient interactions.

AI in healthcare must be trusted, practical and human‑centred. It should automate routine tasks, augment decision‑making with actionable insights, and advance healthcare agility in response to changing demands.

In practice, this means integrating AI into everyday care delivery. AI-enabled image reconstruction and integrated workflows can shorten scan times without changing technologists’ routines. Image-guided therapy platforms can streamline procedures and reduce fragmentation by bringing data, guidance and decision support into a single platform. AI-enabled portable ultrasound systems can accelerate cardiac imaging in emergency departments and support vulnerable patients more effectively. With these technologies supported by structured training and embedded into routine practice, they become reliable clinical tools that standardize processes, reduce variation and improve care quality across settings.

Ultimately, when AI is embedded in daily practice, clinicians are enabled to do what they do best – care for patients. Yet improving productivity alone is not enough. Future-ready health systems must also deliver better outcomes with fewer resources.

Healthcare cannot scale sustainably if it continues to place a strain on people and resources. Delivering better outcomes while reducing environmental impact requires meaningful innovation across the full life cycle, from design and manufacturing to logistics, use and maintenance.

One practical example is helium‑free MRI technology. By eliminating the need for an ongoing liquid helium supply, the systems simplify installation, reduce supply chain risks and enable deployment in resource-constrained or emergency settings. Imaging systems that once required specialized infrastructure can now be installed more flexibly, and services can be restored rapidly in emergent situations – benefiting patients at scale.

Sustainability, however, extends beyond technology. It depends on coordinated action across the healthcare ecosystem and on empowering individuals to take a more proactive role in managing their health. Early prevention and proactive personal health management can reduce the risks of chronic disease and alleviate long‑term system pressures.

Experience in China demonstrates that combining innovation with education, such as in oral health initiatives, can help build healthier behaviours and reduce disease risks over time. Scaling responsible innovations through broad collaboration is essential for building resilient health systems that deliver long-term value and sustainable growth.

Isolated pilot projects, no matter how promising, will not transform healthcare systems. What is needed is a structured pathway that enables continuous testing, validation and scaling of solutions across the healthcare ecosystem. It creates the conditions for meaningful AI innovation, drives sustainable impact, and fosters collaboration between industry, clinicians and policy-makers.

By building and expanding these environments, and learning from real-world experience in markets such as China, we can accelerate the transition to future-ready health systems. Systems that deliver better outcomes. Systems that use resources more efficiently. And systems that give clinicians back the time to focus on the patients who need them most.

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