「元医院」战略启幕 联影如何构建AI资产叙事｜估值叙事18

This article focuses on United Imaging Group’s newly announced "Meta-Hospital" strategy, analyzes its valuation challenges and implementation roadmap, with key takeaways as follows:

1. The core of the is to restructure the healthcare system with AI, rolled out along three cloud-edge-end paths: On the end side, existing imaging equipment will be upgraded to embodied intelligent diagnosis and treatment robots capable of autonomous perception and decision-making; on the cloud side, a multi-modal large model called "Meta-Doctor" will replicate the diagnostic and treatment capabilities of top-tier physicians; on the edge side, wearable devices will extend medical services beyond hospital walls.

2. United Imaging has deep expertise in AI development. Its AI subsidiary United Imaging Intelligence already has AI products deployed in more than 4,000 medical institutions across China covering a wide range of clinical scenarios. It has currently partnered with West China Hospital, a top-tier national tertiary hospital, to jointly build a Meta-Hospital demonstration site.

3. AI currently accounts for less than 0.5% of United Imaging’s total revenue, so the value of its AI business cannot be reflected in existing financial statements, and the secondary market has not yet assigned sufficient AI premium. United Imaging Intelligence plans to unlock the value of its AI assets via an independent IPO.

This article takes United Imaging’s Meta-Hospital strategy as a case study to provide multi-dimensional insights for AI transformation of traditional healthcare brands, with key takeaways as follows:

1. A reference for AI transformation roadmap: As a non-AI-native healthcare hardware brand, United Imaging leveraged its own hardware access to accumulate end-to-end clinical data. It first embedded AI into hardware to achieve product differentiation premium and consolidate its high-end positioning, then gradually upgraded to an independent AI platform. This path aligns with the transformation pace of most traditional healthcare brands.

2. A reference for unlocking brand value: The AI capabilities of traditional hardware brands are rarely valued appropriately under the existing capital market valuation framework. Splitting AI business into independent operations can adapt to the capital market’s valuation logic for AI companies and capture AI premium.

3. The capital market is currently in a valuation window for revenue-generating AI healthcare. Brands can co-build clinical scenarios with top-tier medical institutions to develop demonstration projects that validate their business model and boost brand recognition in the AI healthcare space.

For healthcare industry practitioners, this article sorts out the opportunities, risks and actionable roadmaps in the current AI healthcare track, with key takeaways as follows:

1. Current track opportunities: The capital market is in a valuation window for revenue-generating AI healthcare. Pure-play AI healthcare platform companies can command high P/S premiums. For example, after going public, AI healthcare firm Desay has reached a market capitalization of HK$22.47 billion with a P/S ratio exceeding 130x, reflecting high market recognition of AI healthcare.

2. A reference for commercialization roadmap: In the early stage, companies can leverage mature hardware channels to deploy AI capabilities, and use integrated "device + AI" delivery to reduce implementation risk. They can then gradually transition to independent subscription and MaaS (Model as a Service) models to expand revenue upside.

3. Risk and opportunity alerts: If AI capabilities are only tied to hardware sales, revenue will be constrained by hospitals’ capital expenditure cycles and equipment deployment schedules, making it impossible to capture AI premium. Companies need to build an independent revenue system in advance. Meanwhile, AI healthcare is expanding to cover full-lifecycle care across both in-hospital and out-of-hospital scenarios, and out-of-hospital health management represents a new growth opportunity.

For medical equipment manufacturers, this article draws insights on digital transformation and business expansion from United Imaging’s transformation path, with key takeaways as follows:

1. Product design direction: The core upgrade direction for modern medical equipment is integrating AI capabilities to build embodied intelligent devices with autonomous perception and decision-making. Embedding AI algorithms enables product differentiation premium, consolidates high-end positioning, and effectively improves product market competitiveness.

2. Leveraging inherent business advantages: Manufacturers can accumulate full-cycle real-world clinical data through the equipment they produce and sell. This data is core asset for AI R&D. United Imaging holds over 70% of China’s core medical data via its installed equipment base, giving it an inherent advantage that pure-play AI companies do not have.

3. Insights for digital transformation: Manufacturers do not need to abandon hardware entirely to build pure AI business from day one. They can first complete scenario validation via integrated "hardware + AI" delivery, accumulate technology and customer resources, then gradually expand into cloud services and software services, and extend into out-of-hospital health management scenarios to unlock long-term growth.

For AI healthcare service providers, this article sorts out current industry development trends, client pain points and viable solutions, with key takeaways as follows:

1. Industry development trends: AI healthcare is evolving from single auxiliary functions on individual devices to a systematic "Meta-Hospital" that covers pre-diagnosis, in-consultation, post-treatment care across both in-hospital and out-of-hospital scenarios. Full-lifecycle health management has become the core future development direction.

2. Current core pain points: On one hand, medical institutions need systematic AI capabilities rather than isolated AI functions on individual devices. On the other hand, the capital market is increasingly demanding quantifiable recurring AI revenue, and AI businesses tied to hardware rarely gain proper value recognition.

3. Viable solutions: Pure-play AI service providers can adopt an asset-light MaaS model to directly sell model capabilities, which makes it easier to obtain high premiums from the capital market. Service providers with hardware backgrounds can deploy solutions via existing equipment channels, complete compliance certification in advance, and gradually transition to independent platform-based AI operations. Currently, top-tier medical institutions have demand for co-developing AI and next-generation diagnosis and treatment architecture, creating abundant collaboration opportunities.

For AI healthcare platforms, this article sorts out current industry demand, investment targets and risk mitigation points, with key takeaways as follows:

1. Core industry demand: The AI assets of many traditional healthcare enterprises are undervalued by the existing valuation system, as the manufacturing valuation framework cannot reflect the true value of AI assets. Platforms need to adapt to the valuation characteristics of AI enterprises and help them unlock their value.

2. Sourcing and M&A targets: AI companies like United Imaging Intelligence, which has launched more than 100 AI applications, obtained multiple domestic Class III medical device registrations as well as FDA and CE certifications, completed technology accumulation and compliance layout, reached commercialization readiness, and is preparing for independent IPO, qualify as high-quality investment targets.

3. Operations and risk mitigation: The market currently favors AI enterprises with clear commercial conversion. Platforms can guide AI companies to build a clear recurring revenue system. Meanwhile, AI healthcare is still in the investment phase, and independent commercialization requires three to five years of validation. Platforms should be wary of purely concept-driven projects, and prioritize projects that already have deployed scenarios and full compliance certification.

For healthcare industry researchers, this article discloses the latest industry developments, emerging issues and characteristics of different business models in AI healthcare, with key takeaways as follows:

1. New industry developments: Leading domestic medical technology enterprises have launched systematic Meta-Hospital strategies, proposing to restructure the entire healthcare service system with AI, extending AI capabilities from equipment intelligentization to full-scenario, full-cycle medical services. This is a landmark development in the AI healthcare space, and its proposed overall cloud-edge-end architecture provides a new framework for the systematic development of AI healthcare.

2. Emerging industry issues: AI assets held by non-AI-native enterprises are significantly undervalued under the existing capital market valuation framework. Since AI revenue is tied to hardware and cannot be separated and quantified, these companies cannot capture the AI premium they deserve. Splitting AI business into independent operations is the main solution currently being explored by the industry.

3. Comparison of typical business models: Two mature paths have emerged in the industry. One is the asset-light MaaS model adopted by pure-play AI healthcare companies, which directly sells model capabilities and is more likely to obtain high market premiums. The other is the hardware-backed AI model, where companies transition from embedded AI on equipment to independent platforms, and hold inherent advantages in data and access. The development trajectories of both models deserve continued in-depth research.