While capital markets remain fixated on benchmarking large-model performance, the AI hurricane has already swept through the healthcare industry. Across the ocean, renowned international medical institution Mayo Clinic is teaming up with Microsoft to develop medical large models. Meanwhile, in Hangzhou, China, Shulan Medical, an advocate of computational medicine, is diving headfirst into this arena, launching a bold "AI healthcare" experiment. Starting from 2025, the group announced a strategic upgrade to go "All in on Technology," accelerating the construction of the Liangzhu Future AI Hospital, benchmarking against Mayo Clinic, and aiming to build a "Life Sciences Group" that transitions from "treating disease" to "preventing disease," establishing a technology system centered on computational medicine.

Behind Shulan Medical's AI transformation lies a profound narrative of industrial metamorphosis.

Paradigm Shift and Systemic Reconfiguration

From medical informatization to internet healthcare and now to medical AI, the entire healthcare service industry is undergoing a deep systemic restructuring. Looking back at the trajectory of hospital digitalization—from early financial computerization to HIS (Hospital Information Systems), electronic medical records, laboratory and imaging digitization, and then to internet-based registration, payment, and consultations—the core theme driving this evolution has consistently revolved around "efficiency."

As described by Jie Zheng, founder of Shulan Medical Group, internet healthcare addresses issues of "time and space." It connects patients and doctors, breaks down barriers, and optimizes processes, but at its core, it is merely an online extension of medical services. While it has somewhat bridged information gaps, it has not fundamentally altered the distribution of medical resources—it is essentially an "efficiency revolution."

AI, however, transforms "intelligence." It directly intervenes in core domains that have historically relied entirely on human experts—disease understanding, diagnostic and treatment decisions, research paradigms, and personalized treatment plans. This represents a reconstruction of the "intellectual activities" of medicine. While the internet changed the dimension of efficiency, AI changes the dimension of cognition. This is the fundamental distinction between the current wave of change and the previous "internet + healthcare" boom, and it is also why the disruptive potential of AI far surpasses anything seen before.

This intellectual revolution is arriving faster than anticipated. The year 2026 can be viewed as the "inaugural year for AI healthcare": the AI healthcare track is experiencing concentrated explosive growth, with OpenAI launching ChatGPT Health, Ant Group's "Afu" surpassing 30 million monthly active users, and JD Health's "AI Jingyi" serving over 150 million cumulative users. Jie Zheng believes this signals that as more patients adopt AI applications, the initial impact of AI on the healthcare industry may not come from internal hospital reforms. When patients walk into consultation rooms armed with answers from large models—and oncology patients might even discover overseas clinical trials of new targeted drugs before their doctors—hospitals will increasingly encounter "The Resourceful Patient."

Previously, doctors held the knowledge advantage; now, patients may know even more. The doctor-patient relationship is shifting from "authority-compliance" to "equality-alignment." With the widespread adoption of AI on the consumer side, hospitals no longer face a choice of whether to use AI, but rather the imperative question of how to manage patients who are proficient with AI tools.

For this reason, Shulan Medical's AI strategy differs fundamentally from past internet-based approaches and from most other institutions. Shulan Medical is not developing isolated AI assistance tools; instead, it is undertaking a bottom-up restructuring of the medical organization itself. This reconfiguration manifests across four dimensions: smart services, encompassing pre-hospital triage, in-hospital intelligent navigation, and post-discharge rehabilitation guidance for full-course disease management; smart administration, leveraging AI to enhance operational efficiency across large medical organizations; smart research, empowering clinical investigations by physicians; and smart medical care, utilizing automated imaging report generation and automated medical record writing to free up healthcare professionals' productivity. These four dimensions are interlinked, forming a comprehensive reshaping of the entire medical organization chain, rather than simply affixing an idealistic "AI add-on" to individual components.

This philosophy stems from true "AI-native" thinking. As Jensen Huang noted, many companies merely paste AI onto legacy businesses, whereas AI-native enterprises redesign everything around AI. The differences in efficiency, cost, and potential ceiling between these two approaches are on entirely different scales. The first companies to use AI may not win; only those that complete an AI-native reconfiguration will emerge victorious.

Shulan Group's exploration is not confined to theoretical realms. Its Shulan Liangzhu AI Hospital demonstration zone is slated to be unveiled to the public by the end of 2027. This will be China's leading AI future hospital, built with computational medicine as its underlying architecture and designed as an AI-native platform by a technology-driven medical group. It is not a traditional hospital with AI equipment installed, nor a simple overlay of "traditional hospital + AI tools." Instead, it is a testing ground where operational processes, organizational structures, and business models are entirely reconfigured around AI—a forward-looking exploration of a native AI hospital.

In the future, within the Shulan Liangzhu AI Hospital, patient journeys will be rewritten. Before consultation, the AI agent Dr. Shu (Tree Doctor) will serve as the front-end interface, handling intelligent triage and appointment scheduling. During the consultation, AI will be deeply embedded in clinical workflows, assisting with diagnosis and generating medical records, allowing physicians to dedicate more time to diagnosis and patient communication. After the consultation, AI will consolidate health profiles, help patients with report retrieval, explain results, and manage follow-up prescriptions. In daily life, through a user system that integrates online and offline data, comprehensive lifecycle data management will be implemented, offering users proactive health management services. This long-term, continuous health management approach will become Shulan Medical's differentiated advantage.

Underpinning all this are the computational infrastructure and data feedback loop: real-world clinical data continuously enhances AI capabilities, which in turn elevate medical quality, creating a positive flywheel effect. What grows from this is a business model completely distinct from the "fee-per-visit, done after discharge" model of traditional medical institutions.

Computational Medicine Moves from Theory to Reality

Shulan Medical positions itself as a future life sciences group with global competitiveness. Benchmarking against the "Mayo Clinic of China" is merely a stage-specific milestone. Going forward, Shulan Medical aims to "define its own differentiation," with the core being a "life sciences group"—not just a medical group, but a life sciences platform driven by computational medicine, anchored by AI hospitals, and focused on comprehensive lifecycle health management.

Computational medicine is the technological bedrock of Shulan Medical and its key differentiating label. Its essence is "medicine + modeling." Based on multi-dimensional medical data and computational models, it systematically understands disease mechanisms, human body states, and clinical decision-making, ultimately serving "5P medicine"—predictive, preventive, personalized, participatory, and precision medicine.

Shulan Group is one of the earliest institutions in China to advocate for this cutting-edge research. Its founder and chairman, Jie Zheng, graduated with a computer science background and worked in the medical information industry in his early career, which has given Shulan Group a distinctive technological gene since its inception. The company has also co-established Shulan International Medical College with Zhejiang Shuren University, offering dedicated computational medicine courses and embedding relevant competencies from the very stage of medical talent cultivation. In 2024, the Zhejiang Provincial Key Laboratory of "Artificial Organs and Computational Medicine," co-built with Shulan's participation, was officially approved. Currently, a pilot program for "digital twin" modeling covering the full health lifecycle has been launched, integrating cross-institutional physical examination data, medical records, and multi-omics data such as genomics and proteomics to construct comprehensive lifecycle health profiles.

On top of this foundation, Shulan Group is building two operating systems. One is an AI-native internal hospital operating system, exploring new collaborative workflows for medical organizations using multi-agent systems. The other is a lifelong health steward operating system for users, which will act as a collector, analyst, and advisor for user health data.

Computational medicine also serves as the underlying engine for transitioning from "treating existing disease" to "preventing future disease," representing the strategic high ground for Shulan Group's future. While traditional medicine relies on physicians' personal experience and academic knowledge, computational medicine seeks to evolve each individual's life process into a calculable, predictable model. Jie Zheng believes the core of computational medicine is modeling—advancing from disease models, cellular models, and organ models to a "whole-person model" for each individual. This direction focuses not merely on language generation but on exploring the formation of calculable, trackable, and predictable individual life models through "dynamic whole-person information modeling." He describes this long-term vision as moving "infinitely closer to silicon-based life."

This depends on the integration of a series of interdisciplinary frontier efforts. This concept represents deep thinking about the endgame of medical AI, which may ultimately not be an LLM-style question-and-answer format, but rather a foundational model purpose-built for living systems. Shulan Group is now laying out its position in this direction.

In the past, a person's medical data consisted of a pile of documents—physical examination reports, outpatient records, discharge summaries—scattered across file cabinets in different institutions. But under the framework of computational medicine, each individual's life process will be "twinned" into a calculable, predictable model, transitioning from a document stack to a "whole-person model," similar to the "world model" concept in embodied intelligence. Consequently, the underlying logic of healthcare will shift from "experience-driven" to "data plus model-driven." When models can predict risks and enable early intervention, healthcare will no longer be a passive response of "treating when sick," but rather proactive management. This will be the defining characteristic of Shulan Group's life sciences core.

A New Industrial Narrative

Just as capital markets evaluate AI upstarts like Zhipu AI and Moore Threads, the analytical frameworks for "AI healthcare" and "computational medicine" have completely moved beyond traditional PE/PS metrics. Core AI enterprises transcend past market cognitions in terms of disruptive innovation, technological foresight, uniqueness, and even strategic scarcity. Using the yardstick of traditional medical services to measure a technology-enabled medical group does not align with today's trends.

Shulan Medical's competitiveness and growth potential should be reassessed from three key dimensions. The first is its technological moat. Most medical organizations in China source their HIS from external vendors, leaving technology iteration at the mercy of outside parties. However, since its inception, Shulan Medical has independently developed its own HIS, bringing medical record storage, medication dispensing, meal ordering, and other processes under comprehensive digital management. This system means Shulan Group possesses full data sovereignty and architectural flexibility. It lays a solid foundation for integrating extensive medical data, building whole-process, full-lifecycle electronic medical records, and facilitating imaging retrieval and analysis. Effectively, it acts as a deep groundwork for an "AI hospital operating system"—a foundational capability that is difficult for other medical institutions to replicate in the AI era.

The second is its scarce data assets. In the AI era, authentic, continuous, and standardized medical data is the most scarce strategic resource. Mayo Clinic's 26PB of clinical data underpins its partnership with Microsoft, and Tempus's rise as a star in US-listed medical AI is fundamentally driven by its vast repository of real-world clinical and molecular data. As early as 2015, Jie Zheng founded OMAHA (Open Medical and Healthcare Alliance), a non-profit organization dedicated to standardizing medical data and promoting terminology interoperability. To enable the digitization and computability of medical knowledge, OMAHA has constructed the "Tangram" medical terminology set, aggregating millions of medical concepts, terms, relationships, and various versions of industry resource libraries. Accumulating data assets takes time, but once scale is achieved, it forms the deepest moat.

The third is its application-layer agent ecosystem. With the Dr. Shu AI health agent as the entry point, Shulan Medical is constructing a lifelong health model for every user. In the future, specialized disease agents and workflow agents will synergistically form a "multi-agent ecosystem." This means the commercial imagination is no longer limited to bed counts and outpatient volumes, but extends to the health value throughout a user's entire lifecycle.

"We need to upgrade our original software into AI-native software and collaborate with large models. At the same time, we must comprehensively enhance the closed loop of data governance, data scenarios, and AI environments," emphasized Jie Zheng.

What computational medicine and AI healthcare change is not whether Shulan Medical continues to "treat disease," but rather the boundaries and temporal scales of medical services: the service target extends from a single visit to an individual's entire lifecycle; the service content expands from post-disease diagnosis and treatment to risk prediction, proactive intervention, and continuous health management. Consequently, Shulan Medical's growth space will no longer be determined solely by bed numbers and outpatient volumes, but more by its ability to establish long-term user relationships and consistently deliver health services.

Once this model is validated in real-world operations, the market's assessment of Shulan Medical will go beyond the operational metrics of traditional medical institutions, incorporating dimensions of technological capability, user value, and growth potential. This company is not merely patching up traditional medical scenarios or adding incremental improvements; it is using AI to redefine the underlying logic of healthcare. Its reconstruction of the business model is poised to usher in a true leap forward.