Peking University Health Science Center and ABLE DIGITAL (02687) have officially signed a cooperation agreement to jointly establish the "Peking University Medical Future Learning Center."

In this collaboration, ABLE DIGITAL is deploying its Frontline Deployment Engineering (FDE) team to deeply integrate into operational scenarios, engaging in comprehensive co-construction across the entire project lifecycle—from initiation and pilot testing to review and final delivery—to more effectively grasp client needs.

Beyond Standard Partnerships

The ambition of ABLE DIGITAL and Peking University Medical is to co-create an AI for Science (AI4S) infrastructure for the highly demanding field of medicine. This initiative aims to transform scattered medical knowledge from textbooks, literature, and clinical cases into computable, traceable, and sustainably iterable data assets.

Peking University Medical contributes knowledge assets, clinical scenarios, and expert validation capabilities. ABLE DIGITAL will leverage its self-developed large-scale medical discipline models, a Multi-Agent knowledge production architecture, and multi-modal data processing technologies to jointly produce a foundational "source graph" covering disciplines like human anatomy, pathophysiology, immunology, and genetics.

This will be accompanied by supporting digital resources such as specimen libraries, virtual dissections, digital tissue slides, virtual cells, and virtual patients for experimental research and teaching.

The Value of Structured Data

The data accumulated through this process holds significant value. It is not generic, publicly available information scraped from the web, but structured data meticulously annotated, validated, and interlinked by medical experts and AI systems. It covers a wide spectrum of detail, from macroscopic human anatomy to microscopic cellular morphology and molecular-level mechanism simulations.

In essence, the two parties are co-building a multi-modal "data foundation" for the medical field.

Transforming Medical Knowledge into Data

Medical knowledge is characterized by rapid updates, high professional barriers to entry, and an extremely low tolerance for error. The primary challenge for general-purpose large language models in medical contexts is not a lack of understanding, but an inability to verify correctness or cite specific literature or clinical guidelines as sources.

ABLE DIGITAL's technical approach centers on "evidence-based" reasoning, ensuring every step of the AI's logic is traceable. The system employs a Multi-Agent collaborative framework: human experts set direction and strategy, while AI agents handle large-scale retrieval, comparison, and preliminary organization. The retrieval model is hierarchically designed, first locating high-quality evidence, then summarizing layer by layer, with all generated conclusions required to include explicit source citations.

For example, on a topic like "CD4+ T cell subsets" in immunology, the system would present not just a basic definition, but a connected network of functional characteristics, disease roles, regulatory mechanisms, cutting-edge research, and clinical guidelines, with each node traceable to its original source.

This "generation with traceability" mechanism is not a bonus feature in medicine—it is a fundamental requirement.

Deploying AI Skills Based on Knowledge Data

From service demonstrations, ABLE DIGITAL has already converted these data capabilities into directly usable teaching and research tools, and further into specialized intelligent agents equipped with various skills.

For classroom teaching, the system includes a "Course Super Agent" supporting multi-modal Q&A, real-time dialogue, and private course knowledge base retrieval. An AI-assisted flipped classroom module helps teachers redesign lesson structures. An AI courseware development tool introduces interactive teaching modules to enhance online learning. It can even create custom digital avatar instructors with personalized appearances and voice replication for hyper-realistic lectures and live interaction.

For experimental and skills training, the supporting resource library covers a complete path from basics to clinical application: digital anatomical models and specimen libraries for macroscopic structures, digital slides for microscopic morphology, digital functional models for physiological simulations, virtual cells for molecular mechanisms, and virtual patients for clinical skills.

Students can complete an entire cycle of course learning, experimental operation, independent practice, and competency assessment within the virtual environment. These tools are not isolated; they are designed around a "teacher-student-machine collaboration" concept, providing coherent support from knowledge transfer and skills training to experimental simulation and scientific research.

The FDE Advantage: On-Site Presence for Authentic Data

ABLE DIGITAL's working method in this collaboration is particularly noteworthy. Unlike typical remote software delivery, the company has stationed its FDE team directly at Peking University Medical. These technical personnel are not waiting for requirement documents in an office; they are embedded in teaching and research environments, working side-by-side with frontline educators and clinical experts to co-debug models, validate data, and optimize processes.

The significance of this approach is that only by being on-site can one understand the nuanced, often unarticulated details of medical knowledge production—such as the boundary conditions of a clinical case, the parameter limitations of specific lab equipment, or discrepancies between a textbook and the latest guidelines.

These details constitute genuine industry know-how and form the most difficult-to-replicate parts of a data moat. The FDE model means ABLE DIGITAL is not merely selling software but accumulating non-transferable co-construction experience and data assets at each client site.

This model is more intensive and slower than standardized product delivery, but once established, it creates significantly higher client stickiness and competitive barriers.

Assessing Future Commercial Potential

This collaboration also opens a broader vision: medicine is just the starting point, as ABLE DIGITAL's technological capabilities are extending into wider vertical fields. The development concept for the "Peking University Medical Future Learning Center" explicitly mentions bridging the boundaries between "basic and clinical medicine," "technological innovation and industrial innovation," and "physical and digital spaces."

This implies the future system may serve not only on-campus teaching but also extend to standardized residency training, continuing medical education, clinical decision support, and even pharmaceutical R&D.

For capital markets, the partnership with Peking University Medical represents a strategic, high-level positioning. Once the model is proven in the highly demanding field of medical education, the path for migrating its technological framework to other vertical disciplines like engineering becomes clearer.

The AI4S narrative is ambitious, but its implementation demands extreme precision—detailing how a textbook is deconstructed into data nodes, how a clinical case is linked to cutting-edge literature, or how a virtual cell simulates real molecular reactions. The collaboration between ABLE DIGITAL and Peking University Medical suggests that in China's vertical science sectors, co-building a knowledge infrastructure and accumulating a portfolio of multi-modal data assets may be a highly valuable yet difficult-to-replicate endeavor.