Shanghai, Aug. 11, 2026 (GLOBE NEWSWIRE) -- Frost &amp; Sullivan, a leading global consulting firm, recently released the 2026 White Paper on the Development of the Global Digital and Intelligent Medical Imaging Ecosystem in the AGI Era (the “White Paper”). For the first time, the report systematically examines the medical imaging ecosystem from an industry-wide perspective, covering key stakeholders, service workflows, current challenges, and the policy environment. It focuses on the evolution of medical imaging AI in the AGI era—from standalone algorithms to foundation models, AI agents, and exam-level intelligence—and explores future ecosystem development and internationalization pathways through clinical use cases such as brain, chest, and knee imaging, as well as representative global models. At this pivotal moment, Imagecore, an AI company incubated by Rimag , is featured as a key case study for its closed-loop framework spanning “clinical scenarios, data, standards, foundation models, and exam-level AI agents.” The White Paper analyze Imagecore’s technological approach to “exam-level end-to-end intelligence” and its Hetu Initiative for data infrastructure from three dimensions: industry evolution, depth of technical architecture, and completeness of ecosystem development. Based on the White Paper’s two core criteria for the AGI 3.0 era—“deep, scenario-native integration” and “continuous data-driven evolution”— Imagecore has established core competitiveness among China’s leading medical foundation models and the top tier of medical imaging foundation models. The digital imaging ecosystem jointly developed by Imagecore and Rimag is also recognized as an important model in the global evolution of medical imaging ecosystems. White Paper Defines a New Industry Turning Point: In the AGI 3.0 Era, Competition Shifts from “Algorithm Accuracy” to “Closed-Loop Clinical Integration and Continuous Evolution” As a globally recognized growth consulting firm, Frost &amp; Sullivan provides a systematic industry-wide review of the global digital and intelligent medical imaging sector in the White Paper. By tracing the evolution of AI medical imaging from 1.0 to 3.0, the White Paper highlights a clear progression from “tool development” to “solution integration” and ultimately “ecosystem co-creation.” In the 1.0 stage, fragmented tools focused on a single disease or modality, with limited use cases and no ability to cover a complete examination. The 2.0 stage introduced scenario-level AI products built around clinical workflows, integrating tasks such as segmentation, detection, and report generation. However, these solutions required large volumes of data, involved long development cycles, and were difficult to scale across examination types. In the 3.0 stage, medical imaging foundation models, AI-DIY toolchains, and integrated platforms enable AI capabilities to be developed at scale, continuously iterated, and jointly built across the ecosystem, transforming AI from an external support tool into an embedded capability within clinical workflows. The White Paper states that medical AI has entered the AGI exploration stage and that the basis of competition has fundamentally changed. The focus is no longer on maximizing the accuracy of individual algorithms, but on whether models can be deeply embedded into real clinical workflows through scenario-native design and continuously improve through a data flywheel. Imagecore’s technical architecture closely aligns with the evolution of the AGI 3.0 era, providing a strong foundation for its leading position in both medical foundation models and medical imaging foundation models. Imagecore: Core Competitiveness Among Leading Medical Foundation Models and Top-Tier Medical Imaging Foundation Models (I) Core Capabilities of a Leading Medical Foundation Model Provider in China The White Paper notes that the true industrial value of a foundation model lies not in parameter scale, but in whether it can support end-to-end development of AI applications that are deployable, closed-loop, and capable of continuous self-improvement. Imagecore’s “Dr. Xiaojun 2.0” Brain CT Super Agent is the industry’s first medical imaging AI system to achieve “exam-level end-to-end intelligence.” Trained on nearly 300,000 clinical cases and more than 10 million image-text pairs, it achieves an overall diagnostic accuracy of 87.8%. The product was officially launched at Beijing Tiantan Hospital in April 2026. In real-world testing across more than 30 imaging centers nationwide, over 90% of routine cases required only minor edits before reports could be finalized, demonstrating mature and stable clinical performance. This reflects three core capabilities: end-to-end reasoning across clinical workflows, corresponding to “scenario penetration”; continuous evolution in a specialized domain, corresponding to the “accuracy ceiling”; and standardized, scalable replication, corresponding to “industry breadth.” Based on the White Paper’s criteria for the industrial value of medical foundation models in the AGI 3.0 era, Imagecore has established the key capabilities required to rank among China’s leading players through its combination of a strong foundation model, a closed-loop vertical data system, and scenario-native design. (II) Core Barriers Supporting a Top-Tier Position in Medical Imaging Foundation Models The White Paper notes that while language foundation models are growing rapidly, image-based models—particularly medical imaging foundation models for specialized domains—remain highly challenging and scarce. This is largely due to three barriers: difficulties in compliant data aggregation, the high cost of expert clinical annotation, and severe data fragmentation across imaging equipment. As a result, most companies are unable to establish an effective data flywheel. Imagecore’s Hetu Initiative is designed to address these industry challenges systematically. The White Paper highlights it as a representative example of medical imaging AI infrastructure development. Built for the AI 3.0 era, the Hetu Initiative creates a closed loop across three infrastructure layers. Its standards infrastructure establishes a million-scale standardized semantic system for medical imaging, creating a unified language for imaging data and enabling interoperability across devices and clinical scenarios. Its data infrastructure builds a million-scale, precisely annotated imaging dataset based on real clinical practice, accumulating high-quality core industry data assets. Its model infrastructure uses standardized data and semantic systems to train a multimodal medical imaging foundation model with strong generalization capabilities. As industry competition shifts from “algorithm accuracy” to “closed-loop clinical integration and continuous evolution,” Imagecore has moved ahead from point-based intelligence to system-level intelligence, and from static models to a dynamic data flywheel. Based on the AGI 3.0 industry evolution outlined in the White Paper, this forms the foundation of Imagecore’s top-tier competitiveness in medical imaging foundation models and its ability to maintain a leading position in future AGI-enabled healthcare. Closed-Loop Ecosystems and Global Model Comparison: An Internationalization Path for the China Model As medical imaging data continues to accumulate and AI becomes increasingly integrated into clinical workflows, digital and intelligent medical imaging is evolving from standalone technology applications toward ecosystem development, with different collaboration models emerging globally. The White Paper analyzes three representative digital imaging ecosystem models and outlines future directions for ecosystem development and internationalization. The combination of RadNet and Gleamer represents a model of “imaging service network + embedded AI product portfolio,” leveraging an established outpatient imaging center network to deploy AI capabilities commercially at scale. The I-MED and Harrison.ai model focuses more on joint development between an “imaging services group + AI R&amp;D platform,” with an emphasis on continuously optimizing multi-abnormality detection across large clinical networks. Rimag and Imagecore have developed a third innovative path: a complete digital imaging capability loop spanning real-world clinical scenarios, data governance, standards development, foundation models, exam-level AI products, platform integration, and iterative physician feedback. As the White Paper notes, the distinctive value of this model lies not only in solving the question of how AI products can be deployed in hospitals, but also in addressing a more fundamental industry challenge: how AI capabilities can continuously evolve in real clinical environments. According to the White Paper, the internationalization of China’s digital and intelligent medical imaging ecosystem will gradually evolve from exporting products to exporting collaborative ecosystems. The first stage is product internationalization, bringing clinically validated AI medical imaging software into overseas markets. The second stage is the internationalization of data and standards, including data governance frameworks, structured annotation systems, and core definition standards. The third stage is ecosystem internationalization, integrating platforms, AI, and medical imaging services into a unified overseas offering. With their relatively complete digital imaging capability loop, Rimag and Imagecore represent one viable path in the global exploration of digital imaging ecosystem models. The Frost &amp; Sullivan White Paper marks a clear turning point for the industry. It not only redefines the maturity of medical imaging AI, but also makes clear that in the AGI 3.0 era, sustained leadership will depend on the ability to combine deep understanding of clinical scenarios, strong data assets, and ecosystem integration. Imagecore is advancing along this path, offering a notable China-based model for the development of the global digital and intelligent medical imaging ecosystem. Competition in the true AGI era is only beginning. <pre>CONTACT: Francis, francis.qiu@bjznds.com
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Frost & Sullivan White Paper Marks a Turning Point for Medical Imaging AI: Imagecore Leads the Development of a Digital and Intelligent Medical Imaging Ecosystem in the AGI Era

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