China, August 17, 2026 — Frost & 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 reviews the medical imaging ecosystem from an industry-wide perspective and outlines future directions and pathways for its global development.
At this pivotal moment, Imagecore, an AI company incubated by Rimag, is featured as a key case study in the White Paper, supported by its complete closed-loop system spanning “clinical scenarios–data–standards–foundation models–exam-level agents.” Based on the two core criteria for the AGI 3.0 era defined in the White Paper—“deep, scenario-native integration” and “continuous data-driven evolution”—Imagecore has established core competitiveness among China’s leading medical foundation model companies and in the top tier of image-based foundation models. The digital and intelligent imaging ecosystem jointly developed by Imagecore and Rimag is also regarded as an important force in the global exploration of new medical imaging ecosystem models.
White Paper Defines an Industry Turning Point: In the AGI 3.0 Era, Competition Shifts from “Algorithm Accuracy” to “Scenario Closed Loops and Continuous Evolution”
As a globally recognized growth consulting firm, Frost & Sullivan has, for the first time, systematically analyzed the global digital and intelligent medical imaging industry from an ecosystem perspective in the White Paper.
By tracing the evolution of AI medical imaging from 1.0 to 3.0, the White Paper clearly illustrates the progression from “tool development” to “solution integration” and ultimately “ecosystem co-creation.” The 3.0 stage is centered on medical imaging foundation models, AI-DIY toolchains, and integrated platforms, enabling the scalable production, continuous iteration, and ecosystem-based development of AI capabilities. AI is evolving from an external assistance tool into an embedded intelligence layer within clinical workflows.
The White Paper highlights a fundamental shift as medical AI enters the AGI exploration stage: competition is no longer centered on maximizing the accuracy of individual algorithms, but on whether models can be designed natively around clinical scenarios, deeply embedded into real-world workflows, and continuously improved through data flywheels.
Imagecore’s technology architecture is closely aligned with the direction of AGI 3.0, providing the foundation for its leading position in medical and image-based foundation models.
Imagecore: Core Competitiveness Among Leading Medical Foundation Models and Top-Tier Image-Based Foundation Models
(I) Core Capabilities of a Leading Medical Foundation Model
The White Paper notes that the true industrial value of a foundation model lies not in parameter scale, but in whether it can support an end-to-end production model that generates AI applications capable of closed-loop operation, real-world deployment, and continuous self-evolution.
Imagecore’s “Dr. Xiaojun 2.0” Brain CT Super Agent is the industry’s first imaging AI system to achieve complete exam-level intelligence, with mature and stable real-world performance. It demonstrates three core capabilities: first, end-to-end reasoning across the full workflow, representing “scenario penetration”; second, continuous evolution within a vertical domain, representing the “accuracy ceiling”; and third, standardized, scalable replication, representing “industrial breadth.”
(II) Core Barriers Supporting a Top-Tier Position in Image-Based Foundation Models
The White Paper notes that while language models have grown rapidly, image-based foundation models—especially medical imaging foundation models for vertical domains—remain highly specialized and scarce. This is largely due to three major barriers: the difficulty of compliant medical imaging data aggregation, the high cost of expert annotation, and severe data fragmentation across heterogeneous imaging devices. As a result, most companies are unable to establish an effective data flywheel.
Imagecore’s “Hetu Initiative” is a systematic solution designed to address these industry challenges. The White Paper identifies it as a representative case of medical imaging AI infrastructure development. Designed for the AI 3.0 era, the Hetu Initiative establishes a closed loop through three infrastructure layers: standards infrastructure, which builds a million-scale standardized semantic system for medical imaging and creates a unified language for data exchange across devices and scenarios; data infrastructure, which leverages real clinical settings to build a million-scale, finely annotated medical imaging dataset and accumulate high-quality industry data assets; and model infrastructure, which uses standardized data and semantic systems to train a highly generalizable multimodal medical imaging foundation model.
Building on the data infrastructure established through the Hetu Initiative, Imagecore has productized its data capabilities and now provides clinical-grade multimodal data services to the global market, with a differentiated advantage in fine-grained alignment between 3D CT/MR images and text.
Global medical AI demand for training data is shifting from simple “image + report” pairing toward fine-grained alignment, in which every clinical conclusion in a report must be supported by clearly identified imaging evidence. Overseas markets have established clear specialization in data acquisition (L1) and report structuring (L2), but gaps remain in fine-grained image-text alignment (L3) and longitudinal multimodal evidence chains (L4), particularly for complex modalities such as 3D CT and MR.
Imagecore’s core data capabilities are reflected in four areas:
First, pixel-level fine-grained alignment for 3D CT/MR. Each clinical finding in a radiology report can be accurately mapped to the corresponding CT/MR slice coordinates and lesion contours. Few companies in the open market currently offer end-to-end delivery of this capability for 3D CT/MR.
Second, an AI-driven automated pre-annotation engine. The engine operates directly at the image-text alignment level, automatically identifying findings described in reports and pre-localizing supporting evidence in 3D images, significantly reducing manual annotation costs.
Third, advanced processing of longitudinal multimodal evidence chains. Across multiple imaging examinations for the same patient, Imagecore supports lesion matching across time points, quantitative analysis of changes in volume and density, and treatment response assessment, creating a complete evidence chain from imaging to clinical outcomes.
Fourth, an end-to-end system for producing clinical-grade Ground Truth. This includes medical ontology definition, report structuring, an image-text annotation platform, three-tier quality control—AI pre-screening, junior physician annotation, and senior physician review—and standardized deliverables, ensuring that data is traceable and verifiable.
Imagecore’s core value lies in transforming raw images and reports into trainable, traceable, physician-reviewed clinical Ground Truth. The integration of models and data has enabled Imagecore to move from point-based intelligence to system-level intelligence, creating a competitive barrier that is difficult to replicate.
Based on the AGI 3.0 industry evolution framework outlined in the White Paper, this is both the underlying logic behind Imagecore’s top-tier competitiveness in image-based foundation models and the strategic foundation for maintaining its leadership in the future of AGI-powered healthcare.
Ecosystem Closed Loops and Global Model Comparison: The 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 isolated technology applications toward ecosystem development, with different collaboration models emerging globally. The White Paper analyzes three representative ecosystem models and outlines future development directions and internationalization pathways for digital and intelligent medical imaging.
The RadNet–Gleamer model represents an “imaging service network + embedded AI product portfolio” approach, with its key advantage being the scalable commercial deployment of AI through an established outpatient imaging center network. The I-MED–Harrison.ai model focuses more on joint development between an “imaging services group + AI R&D platform,” emphasizing the continuous optimization of multi-abnormality detection capabilities across large-scale clinical networks.
Rimag and Imagecore have developed a third innovative global pathway: a complete digital and intelligent imaging closed loop spanning real-world clinical scenarios, data governance, standards development, foundation models, exam-level AI products, platform integration, and physician-feedback-driven iteration. As the White Paper notes, the distinctive value of this model lies not only in addressing how AI products can be deployed in hospitals, but also in answering a more fundamental industry question: how AI capabilities can continuously evolve in real-world clinical environments.
About Imagecore
Founded in 2020, Imagecore is a medical imaging AI technology company incubated by Rimag. As an emerging technology enterprise specializing in medical imaging foundation models, agent development, and AI-integrated applications, Imagecore is committed to providing radiologists with AI- and statistics-based medical imaging assistance tools, as well as proprietary tools for in-house medical imaging AI development. Leveraging general-purpose medical imaging foundation models and agent technologies, Imagecore aims to lead the medical imaging industry into the AGI era.
With deep expertise in both healthcare and technology, Imagecore’s founding team brings together leading medical imaging experts, big data specialists, and artificial intelligence experts from China and North America. Imagecore can be understood as the “DeepSeek of medical imaging.” DeepSeek has demonstrated that Chinese teams can achieve breakthroughs in the foundational capabilities of large models. Imagecore has not only developed its own foundation model capabilities in the field of medical imaging, but has also gone a step further by introducing an AI-DIY model and a complete toolchain to address the long-standing challenges facing vertical medical imaging models: difficulty in production, replication, iteration, and real-world deployment.
Media ContactCompany Name: Shanghai Medical Image Insights Intelligent Technology Co., Ltd.Contact Person: FrancisEmail: Send EmailCountry: ChinaWebsite: https://imagecore.com.cn/
