Philips Innovates Healthcare with AI at CIIE 2025: Smart Imaging and Sustainable MRI Transform Care in China

Philips innovates healthcare in China with a clear focus on AI, sustainable imaging, and patient‑centred design. At the 8th China International Import Expo (CIIE) in Shanghai on November 10, 2025, the company highlighted how intelligent technologies are reshaping clinical workflows, diagnostics, and everyday health. With approximately 50 innovations spanning precision testing, smart imaging, and personalised care, Philips underscored its commitment to China’s rapidly evolving digital health ecosystem.

In an interview, Liu Ling, Head of Philips Greater China, noted that AI is already solving practical challenges in medical systems, and China’s appetite for innovation is strong. From radiation‑free MR imaging to AI‑enabled consumer health devices, the showcase illustrated a pragmatic path: deliver safer, faster care while building trust in responsible AI.

Why AI Matters in China’s Healthcare

China’s healthcare system is large, diverse, and under pressure to serve complex patient needs across urban and rural regions. AI can help by improving accuracy, accelerating diagnostic timelines, and reducing administrative burdens. The country has reportedly developed more than 400 large AI models, and regulators have issued over 100 licences for AI health tools, reflecting both technological momentum and a maturing oversight environment.

Market estimates suggest strong growth ahead: China’s AI in healthcare market could rise from 8.8 billion yuan (about US$1.2 billion) in 2023 to 315.7 billion yuan by 2033. This trajectory aligns with national initiatives like the AI Plus plan launched in 2025, which aims to bring AI into testing, diagnostics, and community care to scale access and efficiency.

Key Highlights from CIIE 2025

Philips’ CIIE 2025 exhibit centred on practical innovation—tools clinicians can use today, and systems that hospitals can sustainably run for years. Four themes stood out:

• Smart imaging: The debut of the MR7700 MRI system in China offers clear, radiation‑free imaging designed to support faster exams and advanced diagnostic detail.
• Personalised everyday health: AI‑enabled consumer devices, including an electric toothbrush and shaver, blend health insights with daily routines to encourage better habits and preventive care.
• Integrated hospital workflows: AI‑supported scheduling, triage, and quantification can reduce bottlenecks, helping clinicians focus on patients instead of paperwork.
• Sustainable imaging: A 1.5‑tesla helium‑free MRI built in Suzhou reduces environmental impact and operational complexity. More than 2,000 of these systems are already in use in China and other markets.

MR7700: Smart MRI Arrives in China

MRI is foundational in modern diagnostics because it delivers highly detailed, radiation‑free images. The MR7700 elevates this capability with AI‑enhanced workflows and reconstruction techniques that aim to deliver clarity, speed, and consistency across patient populations. For radiologists and technologists, this can mean more predictable scan times and improved image quality for complex cases, such as neuro, musculoskeletal, or oncology imaging.

Philips reports three‑times‑faster MRI examinations in partner hospitals using AI‑optimised workflows. Faster scans can reduce wait lists, shorten appointment times, and improve patient comfort—especially important for people who experience claustrophobia or have difficulty remaining still.

AI in Consumer Health Devices

AI‑enabled personal care devices address behaviours that drive long‑term health. A connected toothbrush can guide technique and consistency, while an AI‑informed shaver can learn personal preferences for sensitive skin. These tools are not clinical devices, but they can help individuals adopt healthier daily routines, which complements formal care plans and preventive strategies.

AI in Imaging and Diagnostics: What Changes in Practice

When imaging departments integrate AI, the most immediate benefits tend to be efficiency and consistency. Consider three areas:

• Smart scheduling: Automated appointment allocation and pre‑screening reduce delays and better match scan slots with clinical urgency.
• Image reconstruction and analysis: AI can enhance signal processing, support noise reduction, and assist with quantitative measures that are challenging to do manually.
• Triage and reporting: Flagging potential abnormalities for human review can help radiologists prioritise high‑risk cases without skipping essential oversight.

Crucially, medical AI augments rather than replaces clinician judgement. High‑quality systems are designed to support decisions with transparent, reproducible outputs, leaving final calls to qualified professionals.

Faster MRI Workflows and Patient Impact

Philips describes MRI workflows that run up to three times faster in Chinese hospital partnerships. The downstream effects add up:

• Shorter wait times and fewer rescheduled exams
• Improved patient comfort and throughput
• More predictable staffing and equipment utilisation

For administrators, these gains translate to better use of high‑cost imaging assets and more equitable access. For clinicians, they free up time for complex cases and patient communication.

Building Trust: Privacy, Security, and Responsible AI

Healthcare data demands rigorous protection. Hospitals and device makers must align with strong privacy standards, clear data governance, and secure operational practices. Official government resources, such as the broad guidance available through USA.gov, can help organisations identify trustworthy information and find relevant public‑sector materials for compliance and cybersecurity awareness.

Trust grows when vendors demonstrate how data is handled, which models are used, and what performance metrics apply in real‑world clinical settings. Hospitals should request documentation for validation studies, bias analyses across diverse patient groups, and clarity on human oversight.

Cybersecurity Risks and Readiness

As hospitals connect imaging suites, PACS systems, and AI services, they expand their attack surface. Recent reporting on the first AI‑powered cyber espionage campaign underscores why health networks must be resilient. Good practice includes network segmentation, strong identity management, encryption in transit and at rest, continuous monitoring, and tabletop exercises for incident response.

For general cybersecurity resources, organisations can consult official federal guidance portals to navigate programmes, best‑practice materials, and links to relevant agencies. Pairing these resources with vendor‑specific security documentation helps hospitals benchmark their readiness against modern threats.

Privacy‑Preserving Compute and Data Minimisation

Medical AI often requires sensitive data. Privacy‑preserving approaches—such as secure enclaves, differential privacy, and edge compute—can reduce risk while maintaining useful performance. For context on technical approaches that advance private AI workloads without compromising speed, review privacy‑preserving AI compute strategies and how they map to healthcare needs.

Hospitals should emphasise data minimisation, clear consent language, and role‑based access controls. Any AI integration should include robust auditing and the ability to disable or isolate services quickly if anomalies are detected.

Sustainability in Medical Imaging: Helium‑Free MRI

Philips’ 1.5‑tesla helium‑free MRI built in Suzhou reflects a shift toward greener hospital infrastructure. Traditional MRI systems typically rely on liquid helium to cool superconducting magnets. Helium‑free designs reduce dependency on a finite resource, simplify maintenance, and can lower power usage over time. With over 2,000 such systems in China and abroad, the environmental and operational benefits are becoming more visible.

For hospitals managing energy budgets, sustainable imaging isn’t only about carbon footprints. It affects uptime, cost predictability, and resilience—especially when supply chains tighten or energy prices fluctuate.

Collaboration and Localisation: Why Partnerships Matter

Philips is working with multiple hospitals in China to deploy faster MRI systems and co‑develop solutions with multidisciplinary teams. Localisation is critical in healthcare AI. Language, workflow norms, population health profiles, and policy frameworks vary by region. Close partnerships help ensure AI models reflect real‑world data and clinical priorities, and that training and support fit local contexts.

These collaborations should include clear governance: who controls data; how updates and patches are managed; and how performance is monitored. Transparent roles reduce operational friction and speed up adoption.

Market Outlook and Policy Context

China’s AI healthcare market is on a steep growth path—from 8.8 billion yuan in 2023 to a projected 315.7 billion yuan by 2033—supported by the AI Plus plan and ongoing licensing activity for AI tools. The scale of the system and breadth of clinical needs create fertile ground for practical AI.

While China is Philips’ second‑largest market, its importance goes beyond sales. It’s a proving ground for scaled innovation, where patient demand, hospital capacity, and policy priorities converge. Institutions seeking policy guidance can find general information and links to agencies via USA.gov, which provides official portals to public services and regulations in the United States—helpful when aligning cross‑border compliance strategies.

Practical Steps for Hospitals Adopting AI

AI adoption works best with clear objectives and staged deployment. Hospitals can accelerate impact while managing risk by following these steps:

• Define clinical targets: Choose measurable outcomes (e.g., reduced time to diagnosis, lower repeat scans) and set baseline metrics.
• Evaluate performance: Request validation studies, error‑rate documentation, and bias analysis across local patient cohorts.
• Plan infrastructure: Map compute needs, storage, and network resiliency. For budget planning in fast‑moving markets, consider how GPU depreciation and refresh cycles affect total cost of ownership.
• Strengthen security and governance: Implement robust access controls, encryption, audit trails, and incident response procedures informed by official government resources.
• Invest in training: Provide clinicians and technologists with hands‑on sessions and ongoing education to ensure responsible, confident use.
• Pilot, measure, iterate: Start with limited deployments, monitor impact, and scale what works.

Global Context and Comparisons

Healthcare AI is moving quickly worldwide. Generative AI and decision‑support systems are expanding from administrative use to clinical workflows. For perspective on public‑sector adoption, see how AI is being integrated into UK public services with an emphasis on efficiency and data security. Broader industry momentum—captured in roundups like the 2025 generative AI landscape—shows how software, infrastructure, and policy are advancing together.

China’s healthcare AI push is distinct in scale and speed, but the core themes—privacy, safety, sustainability, and measurable clinical value—are universal. That alignment enables cross‑learning and accelerates global progress.

Limitations and Ethics of Medical AI

AI in healthcare must meet high standards. Models can drift, data can be noisy, and health inequities can be reinforced if bias is not actively managed. Ethical deployment requires transparency about how AI works, where it performs well, and where it may fail. Human oversight is non‑negotiable, and clear processes must exist for contesting outputs and escalating atypical cases.

Hospitals should establish review boards or committees to oversee AI deployment, update policies as evidence evolves, and ensure patients understand when and how AI contributes to their care. Pairing technical diligence with strong clinical governance builds durable trust.

Conclusion

At CIIE 2025, Philips demonstrated how AI, smart imaging, and sustainable MRI can accelerate diagnosis, enhance patient experience, and reduce environmental impact in China’s healthcare system. The MR7700’s debut, AI‑enabled daily care devices, and helium‑free MRI systems embody practical innovation—technology designed to solve real problems in busy hospitals and everyday life. As China’s AI health market expands under supportive policy frameworks, the focus should remain on trust, measurable outcomes, and responsible integration. With strong partnerships, proven security, and ongoing clinician engagement, AI can help deliver faster, fairer, and more sustainable care.