The core pivot of the industrial certification of Yinghe Medical Pulse RIMAG GROUP (02522) valuation logic reconstruction.

On August 5, Sullivan officially released the "Global Digital Intelligence Medical Imaging Ecological Industry Development White Paper in the AGI Era." This white paper systematically outlines the underlying logic of AGI technology reconstructing medical imaging services, clinical diagnosis and treatment, and ecological collaboration, and presents empirical applications in typical scenarios such as cranial brain, chest, and knee joints. The white paper identifies Yinghe Medical Imaging as a representative technological force in China's digital intelligence medical imaging ecosystem, clearly stating that RIMAG GROUP and Yinghe Medical Imaging "jointly form the core capability of the closed-loop in the digital intelligence imaging ecosystem, a full-stack closed-loop model that is unique globally." For institutional investors in RIMAG GROUP (02522), this white paper provides a cognitive coordinate to re-examine its valuation logic. The full-stack closed loop constructed by Yinghe Medical Imaging involving "scenarios-data-standards-models-products-feedback" is paving a cognitive pathway for the parent company from service industry valuation to technology platform valuation. Understanding the internal logic of this valuation leap requires a layered breakdown of three progressive core propositions: First, how are RIMAG GROUP's offline assets redefined in the AGI era? Second, what makes Yinghe Medical Imaging's technological path an irreplicable barrier of scarcity? Third, how will this combination's valuation mapping drive the cognitive reconstruction of the capital market? Asset Elevation: Strategic Transition of Offline Imaging Network from "Service Capacity" to "Data Infrastructure" RIMAG GROUP's traditional valuation logic has long anchored on its identity label as "China's first publicly listed third-party medical imaging service company." By the end of 2025, the company has established a network of 117 imaging centers covering 20 provinces, autonomous regions, and municipalities across the country. This heavy asset layout has been viewed linearly in the past, with clear pricing logic from the capital market for such models, but the ceiling is similarly evidentthere are ultimately limited core areas in China's third or fourth-tier cities that can support the independent operation of imaging centers. However, this cognitive framework is being thoroughly rewritten in the AGI era. When the 117 imaging centers are no longer isolated service endpoints but a distributed network continuously generating real clinical scenario data, their strategic significance surpasses the old definition of "examination service capacity." The white paper clearly positions RIMAG GROUP as "the foundation of the ecosystem's scenarios and data""possessing the largest and most diverse third-party imaging center network in China," which "not only provides solid offline examination service scenarios but also leads in establishing an industry-leading, multi-modal imaging data standard system covering CT, MR, DR, ultrasound, and nuclear medicine." This set of standards acts like a "universal language" for the ecosystem, ensuring that vast amounts of medical data can be qualitatively and standardized processed, providing indispensable "fuel" for AI evolution. The value of data lies not in its availability but in its applicability. According to the last report period disclosure, RIMAG GROUP, relying on over 28.53 million imaging data cases, with an average of 20,000 new cases per day and coverage of 12 modalities, has taken the lead in constructing a complete closed loop from data resources to data assets to data transactions. What truly constitutes the competitive barrier is the company's foresight in data standardization since 2021the "Medical Imaging Examination Project Name and Coding Standards" to be released in 2024 covers 11 major categories of coding systems, upgrading data from "raw accumulation" to "trainable AI fuel." This standard has become an important reference model for the National Healthcare Security Administration's "Medical Image Cloud Index," with multiple data products already listed on data exchanges in Shanghai and Beijing, generating tiered data service revenue within various data trust spaces. From a capital perspective, this asset elevation is reshaping RIMAG GROUP's valuation foundation. As data transitions from a "cost center" to a "revenue center," and as the 117 centers upgrade from "service endpoints" to "data infrastructure," RIMAG GROUP's claim as "the world's largest digital intelligence imaging ecosystem leader" is quietly unfolding. Its value measurement shifts from PE to PSmoving from "earning a profit for each store opened" linear growth to an exponential self-reinforcement model of "data appreciating the more it's used, and models becoming more precise with each iteration." This fundamental transformation of asset attributes provides an irreversible industrial premise for RIMAG GROUP's valuation switch. Scarcity Realization: How Full-Stack Technology Closed Loop Reshapes the Competitive Landscape of Medical Imaging AI Having data infrastructure does not equate to possessing AI capability. Transforming raw imaging data into trainable, verifiable, and iteratable intelligent capabilities requires crossing three technical thresholds: data governance, foundational model, and productization closed loop. Yinghe Medical Imaging's technical path constructs a systematic barrier that competitors find difficult to surpass at these three thresholds. The first threshold is data governance. The white paper clearly states that data standardization is "a prerequisite that cannot be overlooked" for AI medical imaging. Yinghe Medical Imaging's Hetu Project is a systematic endeavor addressing this prerequisite. As "the first and currently only systematic data standard construction and data annotation plan in the field of medical imaging," the Hetu Project defines millions of medical imaging standardization semantics through standard infrastructure, creates meticulously annotated datasets through data infrastructure, and supports general AI research and development through model infrastructure, moving medical imaging AI from project-style development around "single disease, single model" to a scalable research and development model centered on examination pathways. The second threshold is the foundational model. Yinghe Medical Imaging's self-developed MIIA foundational model trains a visual foundational model for medical imaging from scratch, enabling the model to have general representation, understanding, and generalization capabilities for CT, MR, X-rays, etc. This is further enhanced by forming multimodal capabilities through image-text alignment. Unlike the lightweight path of directly invoking general large language models or external knowledge repositories, the foundational model acts as "the underlying infrastructure for medical imaging AI," capable of "continuously absorbing new structured data and doctor feedback during real image reading, report revisions, quality control feedback, and the accumulation of new cases, gradually adapting to different hospitals, departments, and doctors' diagnostic habits." The third threshold is the productization closed loop. Transforming model capabilities into clinical value requires embedding them into real workflows and consistent usage. The white paper explicitly identifies one of the current industry's core pain points as "the differences between training datasets and real clinical scenarios, which may lead to subpar product performance in actual applications." Yinghe Medical Imaging's MIIA product system establishes a complete closed loop of "iSpaces generating data iData governing data iResearch training models MIIA-AI embedding into workflow doctor feedback flowing back," allowing AI capabilities to continually evolve through use. Based on this closed loop, the cranial CT superintelligent agent "Doctor Xiao Jun 2.0" can generate a complete structured report within one minute, improving the report writing efficiency by approximately 1.5 times; the chest CT AIR achieves multi-disease recognition in the same scan with a comprehensive diagnostic accuracy rate of 84.6%, and an AI report adoption rate of 86.7%. The white paper positions the combination of RIMAG GROUP and Yinghe Medical Imaging as "a full-stack closed loop model that is unique globally." The cumulative effect of the three thresholdsdata governance requires industry-level standard-setting capability and deep collaboration with authoritative medical institutions; the foundational model relies on continuous data supply, computational power investment, and algorithm teams; the productization closed loop necessitates real clinical scenarios and continuous feedback channels from doctorsconstitutes the systematic barriers that form the underlying logic for the capital market to pay a scarcity premium for its industry position. Valuation Reconstruction: Strategic Migration of Market Cognitive Coordinates from Traditional PE to Technology PS Asset elevation and scarcity realization constitute the industrial premise for valuation reconstructionbut the establishment of industrial logic does not automatically equate to the completion of capital pricing. The switch from PE to PS valuation requires the market to accomplish a re-cognition and confirmation of a companys industrial coordinates, business closed loop, and reference system across three levels. First is the re-anchoring of industrial coordinates. The capital market's pricing for a company essentially reflects its industrial position. RIMAG GROUP received its initial valuation anchor when it went public as "China's first third-party medical imaging service company," based on Sullivan's prior recognition of its industry standing. Now, the white paper positions Yinghe Medical Imaging as "a representative technology platform in the field of medical imaging AI research and application," with its reference point shifting from third-party imaging service providers to leading technology platforms in the medical AI trackYinghe Medical Imaging has built a "globally unique" full-stack closed-loop capacity in the highly valuable and high-barrier vertical field of medical imaging. This migration of industrial coordinates provides a top-level conceptual framework for the valuation switch. The migration of industrial coordinates requires the verifiability of the business closed loop to be realized. Capital market pricing switches cannot be driven solely by concepts; they need quantifiable validation signals. On the data asset level, RIMAG GROUP has taken the lead in completing a full-process exploration from "data resources to data assets to data transactions," with multiple imaging data products being listed and traded in data exchanges, and achieving tiered data service revenue within various national capital platform "data sandboxes." On the AI product level, the cranial CT superintelligent agent "Doctor Xiao Jun 2.0" and chest CT AIR have both entered clinical applications; the comprehensive diagnostic accuracy rate of 84.6% and an AI report adoption rate of 86.7% disclosed in the white paper provide quantifiable clinical validation of product value. Continuous validation of the business closed loop will ultimately drive the market transmission of the reference system migration. In 2025, Tianfengs comparison pricing of RIMAG GROUP with Tempus AI signifies that the market has begun to reassess its valuation based on "data asset operators" rather than "imaging service providers." From industrial signal (Sullivan white paper) to business validation (data transactions + AI product clinical applications) to valuation mapping (Tianfengs first coverage with 8x PS), the three levels of RIMAG GROUP's valuation coordinate migration have all been triggered. When the market acknowledges that a company possesses the most scarce data infrastructure of the AGI era and the full-stack technical capacity to convert data into iteratable AI capabilities, the transition from PE to PS valuation will shift from logical deduction to a pricing fact that is already occurring. Conclusion: In summary, RIMAG GROUP's valuation coordinate system is undergoing a strategic migration of foundational logic reconstruction. At the asset level, the offline imaging network elevates from "examination capacity" to "AGI data infrastructure"; at the technical level, Yinghe Medical Imaging builds a globally unique full-stack intelligence barrier with the Hetu Project, MIIA foundational model, and "work-as-training" closed loop; and at the valuation level, Tianfengs first coverage at 8x PS marks the markets reference system switching from the traditional PE framework to the benchmarking system of healthcare AI data platforms. The white paper defines this combination as a new paradigm of the "digital intelligence imaging ecosystem." The combined value of RIMAG GROUP and Yinghe Medical Imaging has jointly connected the digital intelligence imaging closed loop of "scenarios-data-standards-models-products-feedback," "representing a new path for medical imaging AI from 'single algorithm products' to 'digital intelligence imaging ecosystem'." This not only certifies their industry standing but also signals a significant shift in capital pricing logic. The cognitive switches in the capital market require time, but the direction is clear: when a company simultaneously masters the most scarce data infrastructure of the AGI era and possesses full-stack technical capabilities to convert data into iteratable AI capabilities, the market will ultimately re-measure its value with a new coordinate system.