From Computational Medicine to AI Healthcare, Shulan Medical Unveils a New Narrative for Technology in Healthcare.

As the capital market remains focused on the performance competition of large models, the AI storm has already swept through the medical and health industry. Across the ocean, the internationally renowned medical institution Mayo Clinic is partnering with Microsoft to develop large medical models; meanwhile, in Hangzhou, China, the advocate of "computational medicine," TreeLan Medical, is stepping in to start a grand "AI Healthcare" experiment. Starting in 2025, the group announced its "All in Technology" strategic upgrade, accelerating the construction of the future AI hospital in Liangzhu, benchmarking against Mayo, and creating a "life sciences group" that transitions from "treating diseases" to "preventing diseases," building a technology system centered on computational medicine. TreeLan Medicals AI transformation conceals a profound narrative of industrial metamorphosis. 01 Paradigm Shift, System Reconstruction From medical informatization to internet healthcare, and now to AI healthcare, the entire medical service industry is undergoing a profound systemic reconstruction. Looking back at the evolution of hospital digitization, from early financial computerization to HIS (Hospital Information System), electronic medical records, digital imaging, and then to online appointment booking, payment, and consultations, the core theme of this evolutionary path has always centered around "efficiency." As described by TreeLan Medical Group's founder Zheng Jie, internet healthcare has changed the "spatial-temporal" issues, connecting patients and doctors, breaking barriers, and optimizing processes; its essence is an online extension of medical services. While it has to some extent bridged the information gap, it has not fundamentally changed the distribution of medical resources; the essence remains an "efficiency revolution." AI healthcare, on the other hand, transforms "intellect." It directly intervenes in core aspects such as disease cognition, diagnostic decision-making, research paradigms, and personalized plans that previously relied entirely on human experts, thereby reconstructing the "intellectual activity" of healthcare. The internet changes the dimension of efficiency, while AI changes the dimension of cognition. This is precisely the fundamental difference between this round of transformation and the previous "internet + healthcare" wave, and its disruptive nature surpasses that of the past. The arrival of this intellectual revolution has been faster than expected. The year 2026 could be seen as the "Year of AI Healthcare and Joincare Pharmaceutical Group Industry": The AI healthcare sector has eruptively emerged, with OpenAI launching ChatGPT Health, Ant Groups "Afu" reaching over 30 million monthly active users, and JD HEALTH's "AI Jingyi" serving over 150 million users cumulatively. Zheng Jie believes this means that as more patients use AI applications, the impact of AI on the medical industry may not initially come from transformations within hospitals. When patients walk into consultation rooms armed with answers provided by large models, cancer patients may even find out about new target drug clinical trials abroad sooner than doctors do, leading to an increasing number of "Resourceful Patients" in hospitals. Previously, "doctors knew more," but now, "patients may know more," shifting the doctor-patient relationship from "authority-compliance" to "equality-alignment." With the widespread adoption of AI on the consumer side, hospitals no longer face a question of "whether to use AI," but rather a necessary query of "how to respond to patients who are proficient in using AI." For this reason, TreeLan Medicals AI strategy fundamentally differs from past internet-focused approaches and from most institutions. TreeLan Medical does not develop isolated AI auxiliary tools but rather fundamentally reconstructs medical organizations. This reconstruction manifests across four dimensions: intelligent services that span pre-hospital triage, in-hospital smart guiding, and post-hospital rehabilitation guidance; intelligent management that enhances the operating efficiency of large medical organizations through AI; intelligent research that empowers clinical studies for doctors; and intelligent healthcare that releases healthcare productivity through automated image report generation and automated medical record writing. These four dimensions are interlinked, constituting a comprehensive reshaping of the entire medical organization rather than simply adding an "AI extension" at some stage. This concept originates from truly "AI-native" approaches. As Jensen Huang stated, many companies merely stick AI onto old businesses, while AI-native companies redesign everything around AI. The efficiency, costs, and ceilings of the two are not even in the same league. Those who first use AI may not win; only those who complete AI-native reconstruction first will prevail. TreeLan Group's explorations do not remain at a theoretical level. The TreeLan Liangzhu AI Hospital demonstration area is expected to showcase to the public by the end of 2027, representing the country's leading AI future hospital built by a technology-based medical group with computational medicine as its foundational architecture and AI-native design. Its not simply about retrofitting traditional hospitals with AI equipment nor is it the addition of "traditional hospital + AI tools," but rather an experimental platform where the operational processes, organizational structures, and business models are entirely reconstructed around AI, which can be seen as a forward-looking exploration of a native AI hospital. In the future, the patient care process at the TreeLan Liangzhu AI Hospital will be rewritten. Before the consultation, the AI agent Dr. Shu functions as the front-end entry point, completing intelligent triage and appointment scheduling. During the consultation, AI deeply integrates into the in-hospital diagnostic processes, assisting in diagnosis and medical record generation, allowing doctors more time for diagnosis and communication. After the consultation, the system compiles health records, helping patients with report queries, interpretation, and medication refills; and throughout daily routines, it integrates data communication through Wuxi Online Offline Communication Information Technology Co., Ltd., implementing comprehensive data management across the entire lifecycle and providing proactive health management services. This ongoing health management will become TreeLan Medical's differentiated advantage. Supporting all of this is a computational foundation and closed data loop: real-world treatment data continually feed back into AI capabilities, and these capabilities consistently improve medical quality, forming a positive feedback loop. From this emerges a business model completely different from traditional medical institutions that operate on a "pay-per-visit, leave after treatment" basis. 02 Computational Medicine, Moving to Reality TreeLan Medical positions itself to become a life sciences group with global competitiveness in the future. Using "Chinas Mayo Clinic" as a benchmark is only a temporary reference; in the future, TreeLan Medical must also "define its own differentiation," with the core being "life sciences group," not merely a medical group, but a life sciences platform powered by computational medicine, an AI hospital as the carrier, and aiming for full lifecycle health management. Computational medicine is TreeLan Medical's technical foundation and its core differentiator, which is essentially "medicine + modeling." Based on multidimensional medical data and computational models, it systematically understands disease mechanisms, human states, and diagnostic decisions, ultimately serving the "5P medicine"personalized, precise, preventive, participatory, and predictive. TreeLan Group is one of the earliest advocates of this frontier research in the country. Its founder and chairman Zheng Jie graduated in computer science and has worked in the medical information industry for years, giving TreeLan Group a unique technological gene since its inception. The company has also co-built the TreeLan Xi'an International Medical Investment Institute with Zhejiang Shuren University, which specifically offers computational medicine courses, embedding relevant capabilities from the talent cultivation stage. In 2024, the Zhejiang Provincial "Key Laboratory of Artificial Organs and Computational Medicine," co-founded by TreeLan, was officially approved; currently, the pilot project "DIGIHUMAN," targeting the entire health lifecycle, has kicked off, integrating cross-institutional data such as health checks, medical records, genomics, and proteomics to build comprehensive health archives. On this foundation, TreeLan Group is constructing two operating systems. One is an AI-native internal operating system for hospitals, exploring a new workflow for medical organizations that collaborates through multiple intelligent agents; the other is a lifelong health concierge operating system aimed at users, becoming both a collector and analyzer of health data and offering advice. Computational medicine is also the underlying engine transitioning from "curing existing diseases" to "preventing diseases," representing the strategic high point of TreeLan Group's future strategy. Traditional medicine relies on doctors personal experiences and academic knowledge, while computational medicine attempts to evolve each individual's life process into a computable, predictable model. Zheng Jie believes that the core of computational medicine is modelingfrom disease models, cell models, organ models, to ultimately an individualized "whole-person model." This direction focuses not merely on language generation but on exploring the formation of computable, traceable, and predictable individual life models through "dynamic whole-person information modeling." He describes this long-term vision as "infinitely approaching silicon-based life." This relies on an integration of a series of interdisciplinary cutting-edge works. This concept deeply reflects on the ultimate judgment of medical AI, where the endpoint may not be the LLM-style question-and-answer format but rather a foundational model specifically designed for life systems. TreeLan Group is strategically planning in this direction. In the past, an individual's medical data comprised stacks of documentshealth reports, outpatient medical records, discharge summaries, often scattered across archives in different institutions. However, under the framework of computational medicine, each person's life process will be "twin" into a computable and predictable model, transitioning from piles of documents to a "whole-person model," similar to "world models" in embodied intelligence. Thus, the underlying logic of medicine will shift from being "experience-driven" to being "data + model-driven." When models can predict risks and intervene early, healthcare will transform from passive responses to "treating after becoming ill" into proactive management. This will be the core essence of TreeLan Group's life sciences initiative. 03 New Industrial Narrative Just as the capital market evaluates emerging AI firms like Z.AI and Muxi Co., the analytical framework for "AI healthcare" and "computational medicine" has completely stepped outside the traditional PE/PS framework. Core AI enterprises surpass previous market perceptions in disruptive innovation, technological foresight, uniqueness, and even strategic scarcity. Using traditional healthcare service metrics to measure technology-based medical groups does not align with the trends of the times. TreeLan Medicals competitiveness and growth potential must be reassessed from three dimensions. The first is the technological moat. The vast majority of domestic healthcare organizations' HIS systems come from external vendors, limiting technological iteration; from the beginning, TreeLan Medical has independently developed its information system (HIS), digitalizing the entire process of medical record storage, medication, and meal ordering. This system means that TreeLan Group possesses complete data sovereignty and architectural flexibility, laying a solid foundation for the integration of large medical data, the construction of comprehensive electronic medical records, and image retrieval and analysis, equivalent to establishing a robust base for the "AI hospital operating system," which is a foundational capability that other medical institutions will find hard to replicate in the AI era. The second is the rare data assets. In the AI era, real, continuous, standardized medical data is the most scarce strategic resource; Mayo Clinic's 26PB of clinical data is the basis of its partnership with Microsoft, and Tempus's capability to become a star of US stock market medical AI is also rooted in its vast amounts of real-world clinical and molecular data. As early as 2015, Zheng Jie initiated the establishment of the non-profit organization OMAHA (Open Medical and Health Alliance) to promote the standardization of medical data and terminology recognition. To achieve the digitization and computability of medical knowledge, OMAHA has constructed the "Tetris" medical terminology set, gathering a total of over a million medical concepts, terms, relationships, and various versions of industry resource databases. The accumulation of data assets requires time, but once it scales up, it becomes the deepest moat. The third is the intelligent agent layout at the application layer. Using the Dr. Shu AI health smart agent as an entry point, TreeLan Medical is building a lifelong health model for each user. In the future, specialized disease agents and process agents will collaboratively form a "multi-agent ecosystem." This means that its commercial imagination extends beyond bed counts and outpatient volumes to encompass the health value across the user's entire lifecycle. "We want to upgrade the original software to AI-native software and collaborate with large models. At the same time, the complete upgrade of data governance, data scenarios, and the AI environments closed loop is essential," Zheng Jie emphasized. What computational medicine and AI healthcare change is not whether TreeLan Medical continues to "treat diseases," but the boundaries and temporal scales of medical services: the target service extends from a single visit to a person's entire lifecycle; the service content transitions from treatment after disease onset to risk prediction, proactive intervention, and continuous health management. As a result, TreeLan Medical's growth potential is no longer determined solely by the number of beds and outpatient volumes, but increasingly depends on whether it can establish long-term user relationships and consistently deliver health services. When this model is validated in real-world operations, the market's assessment of TreeLan Medical will not only focus on the operational indicators of traditional medical institutions but will also include dimensions of technological capabilities, user value, and growth potential. This company is not simply patching or incrementing traditional healthcare scenarios but is fundamentally redefining the underlying logic of medicine with AI. Its reconstruction of business models is set to usher in a genuine leap forward. This article is reproduced from the "Market Value Observation" WeChat public account, author: Market Value Observation, GMTEight editor: Xu Wenqiang.