2026 Zhongguancun Forum | Digital China Digital's Tang Kai: The breakthrough point for deep integration of AI + healthcare lies in high-value segments

Source: Global Times

[Global Times Technology Report, Reporter Lin Mengxue] From the sudden emergence of large-model technology in 2022 to the concentrated rise of various AI agents in 2025, AI+ healthcare has also evolved from initial medical imaging recognition to today’s medical intelligent agents deeply embedded into the main clinical workflow, moving toward the critical step from “works” to “works well.”

During the integration process of AI+ healthcare, Tang Kai, Vice President and Chief Engineer of Sync Technology, said during the 2026 Zhongguancun Forum Annual Conference that the real test of this path is whether, “when AI is most needed by doctors, it can deliver an answer that is truly trustworthy.”

The essence of the “last mile” is a value issue

“The last mile of AI+ healthcare is, in essence, a value issue. The key is whether AI can create real value for doctors. If it cannot provide value, everything is bound to become vague.” Tang Kai said. “The point where AI breaks into the mainstream will inevitably land in high-value areas. In clinical care scenarios, high value is especially reflected in clinical challenges such as ‘acute, critical, severe, difficult, complicated, and rare.’”

Tang Kai believes that this year, the application path of AI in the medical field has become increasingly clear, with the implementation pace of technologies represented by large models accelerating significantly in particular. The core marker of this shift is that the R&D and application of medical intelligent agents have become a strong wave of industry development, with the development focus shifting from relatively independent agents toward more complex, deeper business scenarios. “Since 2025, AI has started creating important value at multiple key nodes in medical workflows. Sync Technology and institutions such as Peking Union Medical College Hospital are co-creating in depth. They plan to jointly advance intelligent diagnosis and treatment systems such as MDT (multidisciplinary diagnosis and treatment) based on large models, to assist clinical decision-making for the diagnosis and treatment of difficult and complicated conditions. This also shows that AI technology is continuously moving deeper into the core business areas of healthcare,” he said.

Upholding the core philosophy of “AI for Process” in the AI+ healthcare field—namely, deeply integrating artificial intelligence into business workflows to create tangible value—Sync Technology has made this concept a key guiding principle for technology deployment. “In the medical field, we strictly follow the advancement of medical business workflows. Our current focus is on hospital core diagnosis and treatment workflows, covering the entire perioperative process from preoperative, intraoperative, to postoperative stages, and based on this, we develop a series of intelligent applications.” Tang Kai introduced that, currently, solutions such as diagnosis and treatment of postoperative complications and preoperative anesthesia assessment have already been deployed and put into use in hospitals. The deployment of this entire set of applications is a vivid practice of the “AI for Process” philosophy.

Tang Kai said, “Sync Technology and Peking Union Medical College Hospital have collaborated to develop an intelligent agent for postoperative complications of pancreatic cancer. It can quickly identify the risk of complications, save doctors nearly 80% of their time, and its accuracy has been stable at above 94%.”

For doctors to be willing to proactively use this intelligent agent, the core reasons come from two practical values: “First, it can help doctors cross-verify diagnostic and treatment judgment results, reducing misdiagnosis rates; second, it significantly improves work efficiency.” Tang Kai further pointed out that the rollout of AI in healthcare is not constrained by technical challenges; more importantly, it is about whether it can achieve “small interface, big impact,” meaning that through a lightweight technical entry point, it can produce significant clinical effectiveness. Based on this, he summarized three progressively deeper layers of how AI creates value for healthcare: “The first layer is efficiency-related value, that is, improving doctors’ efficiency and work quality through AI intelligent agents. The second layer is decision-related value, which is an important direction for the future. Doctors’ day-to-day core work is decision-making, and it is a major challenge whether intelligent agents can become a reliable role for assisted decision-making. The third layer is discovery-related value—exploring more cutting-edge areas such as diagnosis and treatment of difficult and complicated diseases through deep collaboration with hospitals. Only by creating value in real diagnosis and treatment workflows can we more completely open up the ‘last mile.’”

Data—the mountain that must be crossed

“As the applications move deeper, we find that the key difficulties are not AI or large-model technology, but data,” Tang Kai said plainly. “The quality of data and the completeness of the data processing workflows will directly determine the depth and sustainability of AI applications.”

To this end, this year Sync Technology has begun proactively cooperating with hospitals to jointly build high-quality medical datasets. “We are carrying out exploratory construction of high-quality disease-specific datasets, centered around various diseases.”

In Tang Kai’s plan, Sync Technology will adhere to a dual-track approach to the AI+ healthcare layout: “One is to continuously deepen at the application layer, pushing AI to play a greater role in core diagnosis and treatment steps. Two is to strengthen the foundation at the data layer, supporting ‘AI for Process’ with ‘Data for Process.’ This is a path that requires long-term investment, and we will continue moving forward in this direction.” This layout also closely matches the currently popular technical concepts of “service twins” and “multi-agent collaboration.”

Regarding the technology development stage of “service twins,” Tang Kai holds a positive and optimistic view, believing that it has already entered the engineering practice stage.

But he also pointed out that the development of service twins must overcome the mountain of data. “The development of service twins, or the advancement of ‘AI for Process,’ is essentially a data problem. Currently, across many industries, the quality of data is still often insufficient to support the deep implementation of such applications.”

Tang Kai used “digital engineering” in manufacturing as an analogy: “In manufacturing, we are pushing ‘digital engineering.’ The core is to build digital twins at the data layer and form detailed device digital portrait models. Only by achieving integration at this level can we expand more designs at the service twins layer.” In the medical field, Sync Technology’s exploration direction is very clear: “Focusing on disease-specific conditions, building a foundational ‘ontological engineering’ layer for diagnosis and treatment processes, and driving ‘AI for Process’ toward maturity through ‘Data for Process.’” He added, “We hope that in the future, we can achieve highly coordinated working scenarios among ‘digital humans, robots, and biological humans (doctors).’”

“From the doctor’s perspective, by 2026, they will gradually feel that AI truly enters the work process, and such intelligent agents will become more and more common. However, from the patient side, the AI-enabled experience that can be clearly felt during the course of seeking care is still limited for now, and this will also be the key direction for deepening applications in the next stage,” Tang Kai said.

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