Shulan Medical's Zheng Jie Sets Sights on 'Life Simulation' Through Computational Medicine and Boundaryless AI Hospitals

Recently, SHULAN HEALTH officially launched Dr. Shu, an AI health agent independently developed by the company. Beyond this milestone, Shulan Group has been intensively exploring AI-driven initiatives since the beginning of this year. To date, SHULAN HEALTH has established strategic partnerships with several data intelligence companies, including Daily Interactive, to deepen its smart healthcare layout and advance the implementation of its future AI-driven medical strategy.

Leveraging AI as a breakthrough tool to expand the boundaries of medical services has become the new mission of Shulan Group. This shift is underpinned by two internal pillars: first, the “dual-wheel drive” technological foundation established at the group’s inception; and second, Zheng Jie, the group’s founder and president. As an IT veteran, this “helmsman” serves as both the architect of the group’s “All-in on Technology” strategic plan and the executor driving the practical implementation of AI applications.

In the process of charting the technological development roadmap for Shulan Group, Zheng Jie adhered to the central theme of “Computational Medicine.” Computational Medicine is not merely a simple aggregation of traditional AI-driven medical tools; rather, it represents a systematic, mechanism-driven paradigm for precision medicine. It encompasses multi-scale modeling of life mechanisms, statistical learning, high-performance computing, and medical knowledge engineering.

On a global scale, computational medicine is exhibiting new development trends. The European Union has launched its 2023 initiative, dedicated to building a federated “Virtual Human Twin” database; meanwhile, the U.S. National Institutes of Health (NIH) Bridge2AI project focuses on constructing multimodal datasets to support precision medicine. In 2024, the U.S. Food and Drug Administration (FDA) significantly accelerated the approval process for AI-based medical devices, and by early June 2025, it had even extended AI-assisted tools to all agency staff. Domestically, SHULAN HEALTH’s strategic layout in computational medicine clearly presents a roadmap progressing from data standardization to life modeling.

In Zheng Jie’s view,Current medical AI is a “subset” of computational medicine, training a knowledge brain based on existing data and cognition to optimize decision-making systems; whereas the ultimate goal of computational medicine is to achieve “life simulation,” one of the prerequisites for which is to pave the way for the circulation of medical data.

A few years ago, the University of California, Los Angeles (UCLA) renamed its 40-year-old Department of Biomathematics to the Department of Computational Medicine, an interdisciplinary field spanning mathematics, computer science, biology, and medicine. Within the international mainstream consensus, “Computational Medicine” is defined as a cutting-edge interdisciplinary discipline centered on multiscale mechanistic modeling of living systems, integrating statistical learning, knowledge engineering, and artificial intelligence technologies. Its core objective,is to build a patient-centered, interpretable, and interactive Patient-Specific Digital Twin, ultimately achieving precision and personalization in medical decision-making.

“Computational medicine is not a new concept; its core lies in developing the capacity for dynamic modeling of life systems, thereby providing more precise insights for clinical decision-making.” Zheng Jie believes that the different stages of development in computational medicine are underpinned by layered iterations of data capabilities.

In hospitals, the EMR system (Electronic Medical Record), more commonly known as “electronic medical records,” digitally documents patients’ visit information. As the next stage of data recording, EHR (Electronic Health Record) serves as a cross-institutional aggregation of healthcare data. Following this is PHR (Personal Health Records), which incorporates not only individual medical records from various hospitals but also personal health information, such as data from smart wearable devices and third-party health examination results.

From EMR and EHR to PHR, the data foundation for computational medicine has been progressively established and advanced. Zheng Jie believes that it will enter two new, progressive stages.The first is the expansion of data volume for the Personal Life Cloud (PLC), and the second is the eventual establishment of a Personal Digital Twin (PDT).

“Unlike the era of paper-based medical records with limited data, health and medical data generated per person annually is now experiencing explosive growth and becoming increasingly fragmented, yet complete data is a prerequisite for delivering precise health and medical services. Although personal electronic medical insurance records have begun to gain widespread adoption, achieving comprehensive and accessible personal health and medical data requires concerted efforts across the entire industry,” stated Zheng Jie.The core of PLC is the establishment of a cloud-based medical and health database centered on the individual.

One promising avenue to explore is granting individuals greater rights to access and use their personal data, namely:Supports online viewing, downloading for local storage, and one-click authorized sharing.The sharing of comprehensive healthcare data serves as a common foundation for industry development. Whether it involves integrating complete datasets or facilitating data circulation, an insurmountable hurdle is the standardized restructuring of data. On this front, Zheng Jie assumes another role.

In 2015, Zheng Jie founded the non-profit organization Open Medical and Healthcare Alliance (OMAHA) as its initiator. OMAHA is dedicated to promoting the development of machine-readable standards for medical and health data, as well as the open-sourcing of these standards. To achieve the digitization and computability of medical knowledge, OMAHA developed the “Tangram” medical terminology set, which aggregates millions of medical concepts, terms, relationships, and industry resources across different versions. This initiative has broken the dependence on foreign medical terminology sets and reduced the development costs for many domestic medical big data organizations.

Specifically, OMAHA has launched a standardized solution framework called the “HiTA Technology Stack,” which comprises a Standard Service Platform, a Knowledge Service Platform, and a Data Service Platform scheduled for official launch in 2025. The Standard Service Platform has achieved mapping and integration with international mainstream medical terminologies (such as SNOMED-CT and LOINC) and healthcare data exchange standards (such as HL7 FHIR), while the Knowledge Service Platform is built upon the “Tangram” medical knowledge graph. In January 2025, the HiTA platform added 93 national and local health information standards to its repository, bringing the cumulative total of documented standards to 924. This technological framework not only significantly enhances healthcare data interoperability but also provides a high-quality, standardized foundation of data and knowledge for constructing dynamic and comprehensive personal health databases, such as PLC and PDT.

From PLC to PDT: The Transition from Static Data to Dynamic Data.Zheng Jie pointed out that PDT is similar to a "digital human," with the difference being that current digital humans are mostly replicas of external representations such as appearance and voice, whereas PDT is the long-term goal of computational medicine development and will approach "silicon-based life" infinitely through "whole-person dynamic information modeling."

In May 2019, Zhejiang Shuren University and SHULAN HEALTH jointly established the Shulan International Medical College of Zhejiang Shuren University. In 2024, the “Zhejiang Provincial Key Laboratory of Artificial Organs and Computational Medicine,” in which the college participates, was officially approved. Leveraging AI models and computational medicine research, the laboratory specializes in developing new technologies for the precise replacement and repair of artificial organs. The college also offers courses in computational medicine, with Zheng Jie serving as a lecturer.

Cultivating more interdisciplinary talents is, in Zheng Jie’s view, another prerequisite for advancing computational medicine, while the rapid iteration and practical application of AI technologies are already adding new dimensions to the field.

Since last year, the development of medical AI has been in full swing.

On one hand, healthcare-specific large language models are proliferating and rapidly being deployed in the core hospital settings of the medical industry. On the other hand, relevant policies have not only identified 84 scenarios for innovative AI applications in healthcare, but the Government Work Report earlier this year also explicitly called for “continuously advancing the ‘AI Plus’ initiative” to accelerate the scaled application of large models in medical care and drug R&D.

“Medical AI falls under the category of computational medicine,” Zheng Jie reiterated,The core of computational medicine is the modeling of biological information, whereas current medical AI tends to focus on the extraction and mining of existing knowledge.Medical large language models are deeply trained on medical knowledge and clinical data; this capability continues to evolve and will play a role in life modeling within computational medicine in the future.

Meanwhile, the investment community is also upgrading its evaluation criteria for medical AI. The value of medical AI no longer stems from the tools themselves, but from the quantifiable outcomes they deliver.MedicineTherapeutic AI should begin as a simple “physician assistant,” continuously evolving to establish a closed loop of high-quality data and physician feedback.This trend aligns closely with Zheng Jie’s emphasis that “computational medicine must ultimately drive the genuine integration of AI into diagnostic and treatment workflows through continuous data accumulation, standardization, and life modeling, thereby achieving precision and personalization in clinical decision-making.”

Shifting from predictions and visions back to the current objective reality, Zheng Jie summarized four major directions for the implementation of medical AI—Smart Management, Smart Services, Smart Healthcare, and Smart Research.

Smart management, used to enhance hospital operational efficiency, is being widely adopted. It has become the primary scenario for the global deployment of healthcare AI, exemplified by the collaboration between the Mayo Clinic and Google Cloud, which leverages Vertex AI to enable intelligent retrieval of medical record data. This trend is also reflected in the evolution of the Hospital Information System (HIS) independently developed by Shulan Health Group.

There is extensive international practice in the field of smart healthcare services. For instance, with funding from the U.S. National Institutes of Health (NIH), the Cleveland Clinic has developed a city-community-level digital twin by leveraging de-identified electronic health records (EHRs) alongside socioeconomic and geographic data, to study health disparities and formulate intervention strategies.

In the realm of smart scientific research, whether AI can evolve into an independent researcher capable of proactively discovering new patterns and theories remains in the early stages of exploration. Internationally, initiatives such as the NIH’s Bridge2AI project in the United States are leveraging voice and imaging data to conduct multidisciplinary research aimed at identifying disease biomarkers. Similarly, computational medicine research at Shulan International Medical College is characterized by its multidisciplinary approach, driving breakthroughs in precise modeling technologies for artificial organs.

In the realm of smart healthcare, current AI applications remain limited; however, some internet healthcare platforms have begun pioneering efforts to create digital twins for medical experts in specific or multiple specialty fields.

Specifically, this digital twin model empowers primary healthcare through telemedicine. Zheng Jie pointed out that while expert-based remote guidance was previously constrained by service radius, digital twins can provide borderless services. Meanwhile, he also emphasized the non-negligible limitations of digital twins: “The rise of ‘famous doctor’ digital twins is an inevitable trend, underpinned by intelligent capabilities for single diseases or specific specialties. Applications have already emerged in certain disciplines, proving particularly well-suited for fields such as psychiatry, internal medicine, and traditional Chinese medicine.”

As an executive at a technology-driven healthcare group, Zheng Jie’s personal experience is that smart services are currently being implemented at the fastest pace, as AI has been the first to enhance user experience.

He stated, “The hospital’s online portal enables direct voice communication with users. As interactions deepen, the backend ‘AI brain’ receives more information, allowing services to become more personalized and akin to intelligent service agents, thereby rapidly and accurately providing consultation, triage, patient guidance, and accompaniment services.”

Aligning Insights with Execution: Focusing on This Smart Service ScenarioSHULAN HEALTH launched the AI health agent Dr. Shu in March 2025, which embeds a series of medical service capabilities covering pre-consultation, during-consultation, and post-consultation stages.

Pre-consultation, patients can describe their symptoms via text or voice; Dr. Shu will quickly identify and match the appropriate department, guiding patients to register accurately. During the consultation, Dr. Shu assists physicians in collecting medical history and automatically generates medical records, thereby improving diagnostic and treatment efficiency while providing clinical recommendations. Post-consultation, Dr. Shu organizes patient health records and features intelligent reminders for follow-up tasks.

Furthermore, Dr. Shu innovatively introduced the “Health Steward” module, which generates personalized health management plans by continuously tracking users’ health data, such as blood pressure, blood glucose levels, and physical activity.

This aligns with the advancement of computational medicine advocated by Zheng Jie, further demonstrating the ongoing implementation of SHULAN HEALTH’s “dual-wheel drive” strategy.

Major international academic institutions, such as Johns Hopkins University and the University of California, Los Angeles (UCLA), have explicitly identified artificial intelligence as one of the four pillars of computational medicine (systems biology, intensive data, artificial intelligence, and high-performance computing). Therefore, medical AI is merely an important tool within the methodological framework of computational medicine, rather than an independent domain.The ultimate goal of future computational medicine lies in bridging the complete pathway from fundamental mechanistic models to clinical decision optimization, with AI serving as the key bridge enabling data-model interaction.Information and intelligent technologies form the foundation for the development of computational medicine.

Since its inception, Shulan Group has established a “dual-wheel drive” development strategy, combining traditional, inclusive healthcare services focused on treating patients and saving lives with a focus on the life sciences sector to build a technology-driven, research-oriented medical group.

In Zheng Jie’s words,“Safeguarding human health while exploring the essence of life.”

Driven by its corporate DNA, SHULAN HEALTH has implemented a self-developed Hospital Information System (HIS), which has enabled the organization to independently digitize processes such as medical record storage, medication dispensing, and meal ordering.

Looking back, that period coincided with the phase when Hospital Information Systems (HIS) were upgrading and iterating from singular “workflow” functionalities to diverse scenarios such as outpatient services and pharmacy management. A number of healthcare IT companies seized this momentum to expand, and there was no shortage of informatization solutions available on the market.

Zheng Jie recalled, “HIS is the most fundamental operating system. At the time, considering the need for rapid iteration capabilities in the future, we chose to develop our own system rather than purchasing an off-the-shelf solution.” In 2016, based on this self-developed system, SHULAN HEALTH launched the International Multidisciplinary Team (iMDT) digital platform for collaborative diagnosis and treatment, enabling cross-border, remote collaboration among top medical professionals from different specialties.

In the decade following the completion of Shulan Health’s data infrastructure, this proprietary system has served as the cornerstone for the integration of AI technologies across various specialized scenarios, including big data ingestion and radiology.

In 2025, Shulan Group further advanced its AI strategy.

Recently, Shulan Group announced an upgrade to its “All in Tech” strategy. Zheng Jie proposed reconstructing the future healthcare model through “Computational Medicine.” In implementing the AI-driven future healthcare strategy, Shulan Healthcare’s Liangzhu Medical Center will evolve into an AI-powered hospital of the future.

It is reported that in July 2024, the topping-out ceremony for the main structure of the Shulan International Medical Center project under SHULAN HEALTH in Liangzhu New City was held. The project is expected to be completed and commence operations in 2026.

For the AI Future Health Medical Center currently under construction, Zheng Jie’s vision is to integrate a more comprehensive healthcare closed-loop with cutting-edge technologies such as artificial intelligence (AI) and wearable devices, thereby delivering customized, “boundary-free, all-scenario” health and medical services throughout the entire lifecycle. In terms of intelligent transformation, it is essential not only to deploy new infrastructure for computing power, networking, and terminals but also to redesign service workflows within care spaces—particularly ensuring seamless integration with AI—to make user experiences smarter and more portable, while connecting physical hospital settings, home and community environments, and virtual online platforms.

To implement its future AI healthcare strategy, SHULAN HEALTH is systematically advancing initiatives from within outward. In addition to the already launched AI service agent, Dr. Shu, the company plans to augment it with specialized agents for weight management and a liver disease physician assistant. Meanwhile, Shulan Group is actively engaging in strategic co-creation with leading partners across various industry sectors. By applying the rigorous quality standards of clinical medicine to the data foundation of high-quality AI healthcare, the group aims to establish a feedback loop between AI medical capabilities and real-world data, thereby enhancing the accuracy and accessibility of AI-driven healthcare.

In summary,Shulan Group’s “All in Tech” strategy is, at its core, a systematic layout centered on computational medicine. It is not merely a stacking of medical AI tools, but a multi-scale, mechanism-driven paradigm for precision medicine, with the ultimate goal of achieving life simulation and truly building the future healthcare ecosystem.

Zheng Jie, who has an IT background, is thrilled by the surging wave of large language model applications. He once believed he would spend his entire career paving the way for the advent of “AI doctors,” only to find that technological advancements have accelerated into the phase of practical implementation. Consequently, he has swiftly shifted from a preparatory stance to active involvement. Amid the rapid development of medical AI, SHULAN HEALTH is going all out.