Sinohealth Holdings' 'AI Alchemy'

Sinohealth Holdings' 'AI Alchemy': Decrypting the Logic Behind Business Growth

Recently, the hottest research report in the market is Goldman Sachs' brand-new AI investment framework for the China stock market.

The report explicitly states that widespread AI adoption could boost Chinese earnings per share (EPS) by 2.5% per year over the next decade. Improving growth prospects and perhaps a confidence boost could also raise the fair value of China stocks by 15% to 20%, and potentially usher in over US$200 billion of portfolio inflows.

These numbers may sound like Wall Street's "standard rhetoric," but the logic behind them is quite solid: AI is not just a show of technical prowess; it is a real "efficiency-printing machine" and a "value catalyst."

For example, in the manufacturing industry, using AI to optimize the supply chain means that cost savings are directly translated into profits. In the financial sector, AI is used for risk control, reducing the non-performing loan rate, which corresponds to an improvement in asset quality.

What about the healthcare industry? The digitalization level in this sector has not been particularly prominent, hiding a significant "scissors gap"—whoever can use AI to turn data into decision-making tools and productivity tools will reap the biggest benefits.

This is why we have recently seen the outstanding performance of AI healthcare stocks in the market. The market expectation gap in this field is also quite large.

Focusing on the company level, Sinohealth Holdings Limited (02361.HK), a Hong Kong-listed company, is, in the author's view, a "hidden miner" standing on this scissors gap and expectation gap.

1. Why Sinohealth is Essential in the AI + Healthcare Bet

In the current global AI wave, Goldman Sachs' attention and investment layout in China's AI field have attracted widespread market attention. Focusing on the industry level, the healthcare sector has become a major highlight. From market perspectives, there are two aspects of potential in this field under the AI umbrella that deserve high attention.

Firstly, the deep integration of AI and healthcare can fully utilize the large amount of high-quality data accumulated in the past, significantly improving diagnostic efficiency and capabilities. Secondly, AI technology empowers new drug R&D, effectively reducing costs, shortening R&D time, and increasing the commercial value of drugs.

Looking at these two directions, the essence still lies in data resources and actual technological implementation. Sinohealth's advantages are self-evident, making it a recognizable AI healthcare stock in the capital market.

In terms of data, the Group is an industry-leader in the accumulation of medical data resources, providing a solid foundation for AI application. It has a data network covering the entire health industry chain, including data from online and offline, in-hospital and out-of-hospital, clinical and health check-up scenarios. These data resources are not only large in scale but also superior in quality and dimension.

The Group's data processing capability is also quite outstanding. It has established a standardized and structured master database, including 38 health industry master database covering pharmaceutical retail, industry supervision, medicine, pharmacy, and life sciences, forming a unified medical and health data structuring standard. In data processing, the machine automatic cleaning rate exceeds 97%, with an accuracy rate of over 99%, and the maximum response speed reaches T+1. This data processing capability enables efficient utilization of its vast data resources to support AI applications.

Looking at technological implementation, the Group is at the forefront of AI application and innovation. For example, its "Tiangong No.1" commercial data smart middleware and "Zhuomuniao" (Woodpecker) smart health management and medical middleware target different application scenarios in the pharmaceutical and medical fields. Thanks to leading data insight capabilities and AI models, customers are provided with standardized SaaS products, customized professional services, and comprehensive solutions.

It can be said that through technological innovation to promote the implementation of AI technology in medical scenarios, significant market competitiveness and new business opportunities have brought to the Group. We will also focus on this later.

Overall, Sinohealth's strong performance in data resources and technological implementation has built a unique competitive advantage in the AI era, allowing it to "stand out" in the current AI + healthcare investment wave. During the entire month of February, its stock price increase once achieved a doubling.

2. How AI Technology Reshapes Sinohealth's Business Logic

Goldman's report mentions that AI improves corporate earnings through three main pathways: productivity enhancement, cost savings, and new revenue opportunities.

Following this logic, Sinohealth's technology-driven business innovation is making efforts in all three areas.

Firstly, the Group's AI transformation is not just for show; it directly targets the core of its business—TO B, TO C, and TO R scenarios, all with significant impact.

For the TO B end, simply put, it allows medical product suppliers and pharmacy owners to "make money effortlessly."

Its AI technology not only provides pharmaceutical and medical equipment enterprises with an integrated smart decision cloud solution that enables customers to establish efficient decision-making capability and improve decision-making efficiency, but also enables pharmacy owners to achieve cost savings and revenue growth through precise inventory management and promotional strategies.

For example, through AI technology, medical product suppliers and pharmacies can predict market demand more accurately to avoid inventory accumulation. Meanwhile, Sinohealth offers a wide range of product portfolio and professional services including data-driven marketing solutions, digital precision marketing plans, and professional training to customers, and strengthens the in-depth interaction between medical product suppliers and pharmacies, as well as between pharmacies and patients. They also help customers quantitatively evaluate the input-output ratio of marketing programs through data insights, thereby optimizing the sales expanse structure, improving the refined management level of sales costs, and enhancing customers' market share and marketing profitability.

For the TO C end, the biggest boon for consumers is that physical examination reports no longer need to be "blind boxes," and full-lifecycle health management is truly becoming a reality.

The Group's AI technology excels in interpreting physical examination reports, providing consumers with more accurate and understandable healthcare advice.

For example, the AI-MDT system can make quick interpretation of physical examination reports and provide users with personalized health management plans, helping them better understand their health status. The "Woodpecker" large model has also topped the CMB ranking list, a domestic authoritative medical large model evaluation platform. At the beginning of this year, it completed a deep integration with DeepSeek, dedicated to creating a "patient-centric" digital full-lifecycle health management system.

It is reported that the deep integration of DeepSeek R1 and the Woodpecker medical large model has built an intelligent decision-making support system. This system combines the case data governance capabilities of Woodpecker with the reasoning advantages of DeepSeek R1. The Woodpecker, with its ability to accurately parse 51 core diseases and 205,000 drug data points, can precisely extract key patient medical history information (such as allergy history and medication records). DeepSeek R1's deep reasoning engine then integrates the latest NCCN guidelines to comprehensively assess patient conditions, providing scientific treatment recommendations, precise drug course suggestions, and comprehensive adverse reaction management recommendations, forming a complete, clear, and standardized treatment plan.

For the TO R end, the most intuitive manifestation is that it can save pharmaceutical companies a decade of detours in R&D.

By analyzing a large amount of drug data through AI technology, pharmaceutical companies can more quickly identify effective drug candidates and accelerate the R&D process. Currently, Sinohealth is also committed to creating a digital empowerment platform for biomedical R&D. It collaborates with major players such as medical institutions, innovative drug enterprises, CRO companies, CMO companies, and pharmaceutical retail enterprises to establish an industrial cooperation ecology. Through digital innovation services, it provides integrated solutions for in-hospital preparations translation, innovative drug and medical devices R&D, foreign products through digital innovation services, and drive the investment, introduction, R&D and promotion of medicine innovation.

It is not difficult to see that from the above B, C, and R ends, the productivity enhancement, cost savings, and new revenue opportunities brought by AI are clear.

Of course, the most formidable aspect of Sinohealth is not the technology itself, but the integration of data, models, and ecosystems into an "iron triangle," completely rewriting the rules of the game in the industry.

It is well known that the medical AI track is crowded with "gold prospectors," but Sinohealth has chosen to be a "road builder." By accumulating "in-hospital + out-of-hospital" data, it has built the "Tiangong No.1" commercial data smart middleware and the "Woodpecker" smart health management and medical middleware. These platforms target different application scenario needs, link various links in the medical and health industry chain, and form a vast ecosystem. This is obviously more difficult to replicate than technology alone.

This approach is also similar to WeChat—you use its system longer, and you become more reliant on its ecosystem. This is also the moat that Sinohealth has built in the AI healthcare track.

3. The Value Revelation of AI Healthcare: The Bigger the Storm, the More Expensive the Fish

The script for AI healthcare is indeed very attractive at present, but investors need to clarify two things:

In the short term, it is more about sentiment, while in the medium term, it focuses on the results of technological transformation and performance realization.

At present, the AI healthcare boom has just begun. The expectation that AI technology will improve the efficiency and revenue of existing businesses for companies like Sinohealth, as well as the increased attention from the capital market on AI healthcare stocks, are expected to drive valuation recovery.

Recently, it can also be seen that the AI healthcare sector has shown strong performance in both A-shares and H-shares markets, with many related companies' stock prices rising. These market performances indicate that the AI healthcare boom has arrived, followed by high investor sentiment, and inevitable significant short-term market fluctuations.

In this regard, Goldman's report notes that optimism over AI is starting to drive “meaningful inflows” to China stocks, and if companies can grow their aggregate market cap by US$3 trillion in the next 12 months, then the AI story could bring in up to US$200 billion in net buying globally. That would help unwind conservative and underweight equity allocations to China stocks by global asset managers.

Although AI healthcare has a high short-term popularity, in the medium term, the market will gradually shift from concept speculation to the verification of technological transformation capabilities and commercial implementation. This means that those with real data accumulation and demonstrated commercial results will further strengthen the logic of the capital market and attract market capital. As mentioned above, Sinohealth already has solid support in this regard.

Looking from a long-term perspective, by building a medical and healthcare industry ecosystem through AI technology, Sinohealth is expected to become an industry leader and capture the most lucrative part of the market, which will also be the true anchor for its valuation leap.

In other words, the commercialization of data elements combined with the deep integration of AI technology is expected to help the Group unleash its long-term growth potential.

Previously, the World Economic Forum's report "The Future of AI-Enabled Health:

Leading the Way" stated that AI is a major transformational force for healthcare. The market is expected to grow at a compound annual growth rate of 43% between 2024 and 2032, reaching a total value of US$491 billion (approximately 3.58 trillion Chinese yuan) by the end of this period.

Currently, the broad application prospects of AI technology in drug R&D, diagnostic treatment, and health management are increasingly favored by the market. Meanwhile, on the policy front, China's State Council and multiple departments have clearly supported the innovative development of AI in the medical field.

For example, in November 2024, the National Healthcare Security Administration included AI-assisted diagnostic technology in the medical service price project guideline. The National Health Commission and other departments jointly released "A Reference Guide to Artificial Intelligence Application Scenario in Healthcare Industry." In December, the General Office of the Shanghai Municipal People's Government issued the "Shanghai Medical Artificial Intelligence Development Plan (2025-2027)." These policies also provide a compliance basis for the future commercial implementation of AI healthcare, bringing new opportunities.

The application of AI technology in the medical field is currently accelerating its penetration, shifting from a single tool to empowering the entire industry chain. The commercialization process of the industry is significantly speeding up. As a pioneer in this field, Sinohealth is expected to be the first to reap the benefits.

Of course, risks cannot be ignored, such as data privacy regulation, technological iteration risks, and slow commercial progress. These concerns are nothing new.

But then again, whose disruptive story is not danced with risks?

4. Conclusion

Sinohealth reminds the author of early Amazon—who could have thought that Bezos would build a global logistics network by selling books? The current medical industry also needs a "road builder" who understands both data and ecosystems.

Goldman Sachs' estimate that AI will add 2.5% to Chinese enterprises' EPS may seem too general. In comparison, Sinohealth's path is much clearer. Data accumulation + model iteration + ecosystem multiplier effect will directly lead to a new burst of its performance, even running an exponential growth curve.

Of course, all of the above still needs time to verify. But for the current market, identifying "real gold" in the early stage of the bubble is more important than escaping before the bubble bursts.

What do you think?