The healthcare AI sector is undergoing a fundamental shift, moving beyond simple tool applications to a stage of deep, native integration that is redefining industry structures. A recent industry white paper highlights this pivotal transition, noting that a new "AI-native health infrastructure" is emerging as the core vehicle for this industry-wide value restructuring.
For the past decade, AI applications in healthcare have largely existed as isolated point solutions. These tools, such as imaging-assisted screening, online triage consultations, and health record documentation, primarily focused on improving efficiency and optimizing costs. However, around 2024, a significant leap occurred. Medical AI began evolving from a supportive tool into a foundational platform that defines the industry's underlying logic.
This transformation is powered by large language models, which are enabling expert-level medical decision-making capabilities. The integration of evidence-based medicine with real-world data is now moving into the industrial implementation phase. Consequently, health management is evolving from static record-keeping to proactive, intelligent agency.
From Passive Records to Active Agents
The white paper identifies a key trend at the user scenario level: the personal health account is being upgraded. It is transitioning from a Personal Health Record (PHR), focused on storage, to a Personal Health Agent (PHA) equipped with analytical, alerting, and recommendation capabilities. Traditional health records are static, fragmented, and lagging, serving mainly to document basic data like exam reports and treatment history. In contrast, an AI health agent leverages intelligent algorithms and continuous learning to achieve real-time data updates, dynamic integration, and intelligent analysis. It can proactively identify potential health risks and output personalized health guidance, risk warnings, and maintenance plans.
This shift indicates a change in the competitive focus of national health management. Future users will require more than a single consultation or an electronic report. They will seek an intelligent gateway that can continuously understand their personal health status, proactively signal risks, and connect them to professional service resources.
The Critical Role of Trust and Evidence
As medical AI gets closer to making real health decisions, trustworthiness becomes the paramount challenge. The white paper emphasizes that the operation of an AI-native health infrastructure relies on four foundational pillars: computing power, data, evidence, and real-world scenarios. The evidence-based pillar provides verifiable professional standards, ensuring the clinical compliance and professionalism of AI-driven decisions. In essence, the competition in medical AI is no longer just about model capability; it is increasingly about the strength of the evidence chain, traceability, verifiability, and the ability to deploy in authentic scenarios.
Qingsong Health as a Case Study
Within this evolving landscape, Qingsong Health (02661.HK) is highlighted in the white paper as a significant example of China's AI-native health infrastructure. The report notes that the company empowers its entire business line with its self-developed AIcare technology stack. It has constructed a comprehensive AI-native health service system covering user, content, marketing, research, and enterprise service chains through its Nebula digital base, Dr.GPT medical vertical large model, and the evidence-based medicine intelligent agent named "Zhengyuanfang."
"Zhengyuanfang" serves as a crucial tool for Qingsong Health in advancing trustworthy medical AI. Functioning as a professional hub, it handles core tasks such as medical evidence retrieval and clinical guideline comparison, ensuring all outputs are traceable and evidence-backed. Furthermore, it incorporates a "System 2 (slow thinking)" mechanism that mimics the clinical reasoning of senior physicians. This establishes a deep clinical decision support pathway of "evidence first, conclusion later," elevating AI's role from an "information provider" to a "clinical decision partner."
The report discloses that in May 2026, "Zhengyuanfang" became the first medical health intelligent assistant product in China to pass the authoritative "MedClaw" evaluation by a leading industry institute, receiving recognition across 13 key metrics including evidence-based Q&A. It also achieved a perfect score with 100% accuracy on the 2023 Chinese Practicing Physician Qualification Exam and secured state-of-the-art (SOTA) results in senior oncology specialist exams.
Integrating AI into Clinical and User Workflows
On the physician side, Qingsong Health is embedding evidence-based AI into real clinical workflows. The company has launched the "Zhengyuanfang·MedClaw Collaboration Body," which integrates the evidence-based medicine agent with a multi-agent framework. This drives the coordinated operation of various agents for task planning and content generation, consolidating cumbersome multi-step operations into automated workflows. With the integration of "Zhengyuanfang" into the professional doctor service platform "Yilu Qingsong," Qingsong Health has achieved product synergy between its proprietary AI capabilities and its dedicated physician service network.
As of March 31, 2026, the "Yilu Qingsong" platform, empowered by "Zhengyuanfang," served 69,615 medical professionals, a year-on-year increase of 46.4%, with over 52.7% being associate chief physicians or higher.
On the user side, Qingsong Health utilizes products like Dr.GPT to provide users with more understandable and actionable health decision support. Leveraging its service ecosystem covering "examination, medical care, pharmacy, rehabilitation, and insurance," the company's AI capabilities extend beyond a simple Q&A interface. They further connect various stages including early screening, health education, doctor services, insurance protection, and long-term health management, driving the upgrade of personal health accounts from static records to active health agents.
Redefining Corporate Value in the AI Era
The white paper also suggests that changes in the industry's foundational logic will lead to updates in corporate valuation standards. The capital market's pricing logic for medical AI companies is expected to gradually shift from a focus on scale and traffic to a focus on AI-native infrastructure, rapid growth, self-sustainability, and ecological closed loops.
The report posits that Qingsong Health, with its full-stack AI-native technology base and product matrix, its trustworthiness moat built on evidence-based medicine, and its closed-loop system integrating "payment, service, and data," has become one of the few platform-based companies in China that possesses AI-native capabilities, a national-level account foundation, and an ecological closed loop.
Industry observers believe that as medical AI transitions from tool application to native re-architecture, companies possessing real-world scenarios, evidence-based capabilities, a substantial user base, and a closed-loop payment-service system are likely to attract greater attention in the next wave of industry re-evaluation. For Qingsong Health, its AI strategy is expanding beyond single-product innovation to encompass the construction of national health management infrastructure, suggesting continued potential in areas like health agency, physician empowerment, insurance synergy, and the spillover of AI capabilities.
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