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Recently, Frost & Sullivan (hereinafter referred to as "Frost & Sullivan") officially released '2026 AGI Era Global Digital and Intelligent Medical Imaging Ecosystem Industry Development White Paper' (hereinafter referred to as "White Paper"). The White Paper systematically reviews the current status and future trends of the global and Chinese medical imaging ecosystem, comprehensively analyzes the core participants, service processes, practical challenges, and policy environment of the medical imaging ecosystem. It focuses on studying the enabling logic of artificial intelligence for the medical imaging ecosystem, and systematically presents the development path of medical imaging AI, which evolves from single-disease, single-task auxiliary tools to intelligent inspection project-level, medical imaging base model, and AI Agent. At the same time, the White Paper analyzes the application practices of AI in imaging diagnosis, report generation, quality control, and clinical collaboration based on cranial, thoracic, knee joint, and other representative clinical scenarios. It further explores the construction model, internationalization path, and future development direction of the digital and intelligent medical imaging ecosystem, aiming to provide industry insights and trend analysis for the industry, medical institutions, investment firms, and related professionals.
Released on Frost & Sullivan's official website
The English version of the White Paper ('2026 Global Digital and Intelligent Medical Imaging Ecosystem in the AGI Era White Paper') was simultaneously released globally, and was disseminated through multiple channels regarding the international medical imaging, medical artificial intelligence, and digital healthcare industry ecosystem. Leveraging Frost & Sullivan's global research and communication network, this English version of the White Paper was further promoted to industry institutions, professional media, medical technology companies, and investors in North America, Europe, Asia-Pacific, and other major markets, enhancing global understanding of China's development of the digital and intelligent medical imaging industry, the evolution of medical imaging AI technology, and ecosystem construction practices.
With Frost & Sullivan's long-term industry research expertise in healthcare and artificial intelligence, the global release of this White Paper helps systematically demonstrate China's progress in medical imaging data governance, AI technology research and development, clinical scenario applications, and digital and intelligent ecosystem construction. It enables global industry participants to better understand the development trend of medical imaging AI, which evolves from individual tools to platform-based, systematic, and ecosystem-oriented models. Additionally, by reviewing the global representative medical imaging services and AI collaboration models, the White Paper provides references for Chinese enterprises to carry out international cooperation, technical exchanges, and overseas market expansion, and offers an industry research perspective for the continuous construction and innovative development of the global digital and intelligent medical imaging ecosystem.
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Zhang Chenyang, Senior Consultant Director of Frost & Sullivan China's Healthcare Division
01
Overview of the Medical Imaging Ecosystem: From Dispersed Links to Full-Link Collaboration
Medical imaging is evolving from a single inspection tool into a comprehensive diagnostic capability:
Medical imaging visualizes the human body's structure, functions, and pathological features through X-rays, electromagnetic fields, ultrasound, and radionuclides. Technologies such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, digital subtraction angiography (DSA), and nuclear medicine imaging have been widely used in disease screening, clinical diagnosis, treatment planning, efficacy assessment, and follow-up management. With continuous upgrades in equipment performance, medical informatization, and artificial intelligence technology, the industrial value of medical imaging is expanding from "performing one examination" to "supporting a complete diagnostic process."
In the future, the medical imaging ecosystem will further form a comprehensive capability system of "scenario + data + AI + platform": real imaging service scenarios provide an entry point for AI, high-quality imaging and report data form the basis for model training and validation, AI enhances key capabilities such as order generation, scanning, diagnosis, and clinical application, and the platform connects data, models, doctors, and medical institutions, enabling different entities to collaborate within a unified workflow.
Multiple parties participate and provide two-way feedback, forming the digital and intelligent medical imaging ecosystem:
The digital and intelligent medical imaging ecosystem is not composed of a single hospital or a single technology company. It involves people with medical imaging inspection needs, hospitals and third-party imaging centers as service providers, medical imaging equipment suppliers, AI medical imaging solution providers, and medical informatization infrastructure providers. Equipment suppliers support imaging collection, service providers handle inspections and diagnoses, informatization companies ensure data storage, transmission, and interconnection, AI companies provide intelligent capabilities in pre-examination management, during-examination collaboration, and post-examination support, and the real usage feedback from patients and doctors further promotes product optimization.
Figure: Digital and Intelligent Medical Imaging Ecosystem and Collaboration Relationships among Related Parties
Source: Frost & Sullivan Analysis
The quality of imaging services depends on the overall completion of order generation, scanning, diagnosis, and clinical application:
Medical imaging services are not simply a process of "taking images—generating reports"; instead, it involves a continuous workflow in which clinical doctors select examination items, technicians perform image acquisition and quality control, radiologists identify signs and make diagnoses, and clinical doctors convert results into treatment decisions. Any weak performance at any stage can reduce the overall value of the imaging results. For example, inappropriate selection of examination items can affect subsequent diagnostic basis, irregular image acquisition can limit lesion identification, inaccurate reports or unclear descriptions can lower clinical reference value, and failure of clinicians to correctly understand and apply imaging results also impacts treatment decisions.
Imaging capabilities are not the abilities of a single step, but a comprehensive ability system that spans the entire medical imaging service process, including clinical doctors' decision-making ability in examinations, technicians' image acquisition and quality control ability, radiologists' diagnostic and reporting ability, and clinical doctors' ability to apply imaging results. The capabilities of each stage are reflected in their completion level, and together they determine the final quality and value of medical imaging services.
Figure: Breakdown of the medical imaging service process and imaging capabilities
Source: Frost & Sullivan Analysis
Medical institutions at different levels face distinct challenges, and the demand for digital intelligence exhibits clear stratification:
Top-tier tertiary hospitals and regional central hospitals typically have strong equipment and talent foundations, but they face issues such as high patient flow, high reading workload, and increasing pressure from regional quality control and medical consortium management. Their core needs include AI-assisted reading, report efficiency improvement, intelligent quality control, and regional capability delivery. Third- and fourth-level hospitals have routine examination capabilities, but there is a shortage of high-level radiologists. For complex cases, they still rely on higher-level hospitals, and their needs focus on auxiliary diagnosis, structured reports, remote review, and sub-specialty capability enhancement. Grassroots and county-level hospitals face issues such as insufficient equipment utilization, shortage of technicians and diagnostic doctors, inconsistent collection and report standards, and difficulty in result recognition. They need remote diagnosis, standardized collection, AI preliminary screening, and grassroots quality control capabilities.
Chart: Medical imaging ecosystem pain points and needs across different levels of medical institutions in China
Source: Sullivan Analysis
Policy direction has shifted from "supplying equipment and expanding supply" to "strengthening grassroots levels, promoting recognition, establishing standards, and introducing AI":
In recent years, policies related to medical imaging in China have been continuously improved. The management of large-scale medical equipment configuration provides institutional basis for standardized configuration of high-end imaging equipment; policies such as mutual recognition of inspection and test results, tight-knit county-level medical communities, and the "Thousand County Project" promote "grassroots inspection, superior diagnosis, and result sharing"; the project guidelines for radiation examination and ultrasound medical service price items promote standardization of charging items; the addition of the "Artificial Intelligence Assistance" extension reflects that AI is gradually entering the discussion of standardized clinical application and payment systems.
Policy evolution aligns with industry pain points: Top hospitals need efficiency and regional management capabilities, third- and fourth-level hospitals need diagnostic capability enhancement, and grassroots institutions need to補 basic services and improve recognition levels. The construction of a digital medical imaging ecosystem is shifting from single-point equipment investment to a systematic engineering involving regional collaboration, process standardization, data governance, talent empowerment, and AI application.
Data standardization and assetization have become the basic outcomes of ecosystem construction:
Medical imaging data features a large volume, multiple modalities, significant equipment differences, complex disease types, and inconsistent report expressions. For AI research, ungoverned raw images and free-text reports cannot be directly transformed into high-quality training data. Only through standardization of inspection items, structuring of reports, semantic normalization, professional annotation, and multi-level quality control can scattered data be transformed into trainable, verifiable, and reusable data assets.
02
The enabling logic of AI in the medical imaging ecosystem
Medical AI has entered the AGI exploration stage, and the core competition has shifted from algorithm accuracy to scenario integration and continuous evolution:
Medical AI development has gone through stages such as statistical learning, deep learning, and large language models. In the early stage, systems relied on manual rules and feature engineering. Deep learning enabled image recognition, lesion detection, and disease classification for clinical assistance. Large language models significantly improved the efficiency of medical knowledge retrieval, medical record summarization, and text generation. After entering the AGI exploration stage, AI begins to possess stronger complex reasoning, multi-modal perception, task planning, and feedback correction capabilities. Its value is no longer limited to answering questions but has the potential to enter real diagnosis and treatment processes.
AI Agent is an important application form in this stage. The model acts like a "smart brain" providing understanding, reasoning, and generation capabilities, while the Agent breaks down tasks around specific goals, calls tools, executes processes, and optimizes feedback. In the medical imaging scenario, the Agent can further connect image retrieval, lesion analysis, report generation, quality control, doctor review, and model iteration, turning AI from an external assistant into an intelligent collaborator in the workflow.
Medical imaging AI should be defined by "scenario—role—task", rather than just by disease type or algorithm:
Medical imaging AI can be divided into four core application scenarios: AI that assists clinicians in ordering examinations, AI that assists technicians in scanning and image quality control, AI that assists radiologists in diagnosis and report generation, and AI that assists clinicians in diagnosis, treatment, and decision support. This classification directly corresponds to the real service process and is more conducive to evaluating the actual contribution of AI to the rationality of examinations, image quality, reading efficiency, report standardization, and clinical decision-making values.
Medical imaging service quality is determined by multiple links such as ordering, image acquisition, diagnostic analysis, and clinical application. By empowering AI over each link of the process, the accuracy of examination decisions, image acquisition standardization, diagnostic efficiency, and clinical application value can be further improved, achieving a comprehensive improvement of medical imaging capabilities from single-link optimization to full-link collaboration. Based on the benchmark that all four core capabilities reach 90%, after AI empowerment, each link improves to approximately 99%, and overall imaging capabilities increase from about 66% to about 97%, a 31 percentage point improvement. These 31 percentage points mean fewer missed diagnoses, fewer misdiagnoses, better patient experience, and more efficient resource utilization—this is the core value of full-link AI empowerment.
Chart: Image capability improvement through full-link AI empowerment
Source: Sullivan Analysis
Industry evolution: From AI 1.0 fragmented tools to AI 3.0 ecosystem empowerment platform:
AI medical imaging 1.0 stage mainly developed models for single organ, single disease type, or single task, capable of providing assistance in clear scenarios such as pulmonary nodules, fractures, and bleeding, but the products were isolated from each other and could not cover the complete reading process. The 2.0 stage began with inspection items and business paths as units, integrating tasks such as detection, segmentation, quantitative analysis, and report generation into scenario-level products, closer to the actual work of doctors. The 3.0 stage focuses on a medical imaging base model, AI-DIY toolchain, and integration platform, promoting batch production, continuous iteration, and ecosystem building of AI capabilities.
Chart: Industry evolution of AI medical imaging from 1.0 to 3.0
Source: Sullivan Analysis
The medical imaging base model has become an important foundation for general intelligence:
Traditional models used a development paradigm of "single dataset—single model—single task". Every time a new disease type or task was added, it was necessary to re-collect, annotate, train, and verify data, resulting in long development cycles and high expansion costs. The medical imaging base model is pre-trained on large-scale, multi-modal imaging images, diagnostic reports, structured labels, and clinical semantics, forming a general understanding of anatomy, lesion characteristics, and report language. It can quickly expand to downstream tasks such as detection, segmentation, classification, and report generation through small-sample fine-tuning, task adaptation, or tool invocation.
The core barrier of the base model lies not only in algorithms but also in whether a company can form a closed loop of "real scenario—high-quality data—standard governance—model training—clinical feedback". Feedback from doctors during reading, report modification, and quality control review can further be converted into structured training signals, enabling the model to continue evolving in real applications.
Hetu Plan: Consolidating the foundation of AI research with data standards and professional annotation:
Yinghe Yimai is a platform under Yimanyang Focused on the research and application of medical imaging artificial intelligence technology. Relying on Yimanyang's long-term accumulation of medical imaging service scenarios, data resources, and professional capabilities, it further carries out medical imaging data governance, model training, and AI application development, promoting the digitalization of the medical imaging service system. Based on this, the Hetu Plan is jointly promoted by Yinghe Yimai, Yimanyang, and cooperative medical institutions. The Hetu Plan is a systematic data standard construction and data annotation plan for the AI 3.0 era in the medical imaging field and a representative medical imaging AI infrastructure construction case in the industry. Different from traditional single-point data annotation projects, the Hetu Plan does not focus on a single disease type or model but starts from the industry's underlying standards, systematically planning the standards, semantics, datasets, and model capabilities required for medical imaging AI. Its core goal is to implement "three major infrastructure": defining million-level medical imaging standardized semantics through standard infrastructure, creating million-level highly annotated datasets through data infrastructure, and building full-modal base model capabilities through model infrastructure.
Research path in the AI medical imaging 4.0 era: There is no "universal AI"; inspection items become the core research unit:
Medical imaging involves multiple modalities such as CT, MRI, X-ray, ultrasound, and nuclear medicine. Different inspection items have significant differences in scanning methods, anatomical structures, disease spectra, and clinical purposes. A complete examination usually also includes multiple tasks such as anomaly identification, lesion localization, quantitative analysis, differential diagnosis, and report generation. Therefore, the development of medical imaging AI does not rely on one model to solve all problems but continuously builds a large number of specialized AI capabilities around different inspection items and clinical scenarios.
In the AI medical imaging 1.0 stage, products mainly focused on single organ, single disease type, or single task, capable of completing local lesion identification, but the application scenarios were relatively fragmented. The 2.0 stage began with complete inspection items and business paths as units, combining tasks such as lesion detection, segmentation, quantitative analysis, and report generation. After entering the 3.0 stage, the base model, research toolchain, and AI Agent further reduce the development threshold of professional AI products, enabling the industry to move from a few single-point product developments to continuous production of many inspection item-level AI capabilities.
Ecological building is the inevitable path, and doctors are transforming from AI users to AI creators:
There are a large number of inspection items and clinical needs covered by medical imaging, and a single technology company cannot independently understand and meet all niche scenarios. Future research models will involve more participation from doctors, medical institutions, and AI platforms: doctors propose clinical questions, define tasks, and set standards, medical institutions provide real scenarios and data feedback, and the AI platform provides a base model, data governance, annotation, training, verification, and deployment tools.
Under this model, AI Agent needs to clearly define the specific service targets and responsible entities. Doctors who best understand inspection items, diagnosis processes, and clinical needs are suitable to be the main definers and continuous optimizers of professional AI capabilities. AI-DIY does not require doctors to master complex programming skills. Through scientific research and model development platforms, doctors' diagnostic experiences, judgment logic, and operation processes formed in daily work are transformed into structured data and trainable tasks, allowing doctors to participate in data sorting, intelligent annotation, model training, and effect verification.
As AI is continuously used in real scenarios, doctors' reading results, report modifications, and diagnostic feedback can further be converted into model optimization basis, promoting professional experience to be precipitated into replicable and iterative digital capabilities, achieving "work as training, use as evolution".
Chart: Doctors participating in medical imaging A
I's developed AI-DIY model
Source: Sullivan Analysis
AI integration platform promotes dispersed capabilities into clinical workflows:
As the number of medical imaging AI products increases, single-point deployment and independent invocation modes are likely to cause system fragmentation, complex operations, and increased maintenance costs. The AI medical imaging 3.0 era requires a unified integration platform to connect self-developed and third-party AI capabilities into imaging information systems and automatically match corresponding tools based on inspection items, imaging modalities, and clinical needs.
AI integration platform connects doctors, medical institutions, and AI research achievements on one side, and actual work flows such as image browsing, report editing, quality control, and remote collaboration on the other side. Doctors do not need to switch between multiple systems, and can use AI capabilities in the original reading and report environment. Through unified management, on-demand invocation, and continuous update, the platform can improve the clinical accessibility of AI products and provide a foundation for business models such as project construction, platform operation, inspection services, data services, and AI capability output.
Chart: AI integration platform connecting research achievements and clinical application scenarios
Source: Sullivan Analysis
Data-driven continuous iteration forms a digital and intelligent imaging capability closed loop:
Medical imaging AI is not a static product delivered once trained. It needs to be continuously iterated based on real medical scenarios. Different data in medical imaging services accumulate the capabilities of different professional roles: ordering data reflect the clinical doctors' choice logic for inspection items, imaging data reflect the equipment imaging and technician operation capabilities, diagnostic report data carry the radiologist's judgment experience, and image post-processing and clinical application data reflect the actual value of imaging results in treatment decisions, efficacy evaluation, and patient management.
By standardizing the governance of the above data and continuously returning feedback from clinical use to the model research link, a closed loop of "clinical application—data generation—model optimization—capability upgrade—reapplication" can be formed. AI is no longer just an external auxiliary tool but gradually integrates into the medical imaging service system, promoting the joint improvement of all links such as ordering, scanning, diagnosis, and clinical application.
Chart: Imaging capabilities, data, and AI form a continuous closed loop
Source: Sullivan Analysis
Prospects for AI medical imaging 4.0 era: Moving towards full-scenario, full-modal, full-disease-type, and full-process:
On the basis of the continuous enrichment of inspection item-level AI capabilities in the 3.0 era, AI medical imaging will further evolve into the 4.0 era. Its core is not simply adding the number of models but realizing the coordination of different AI capabilities, enabling AI to cover complete processes such as intelligent ordering, imaging acquisition, image quality control, auxiliary diagnosis, report generation, treatment decision support, efficacy evaluation, and follow-up management.
Future medical imaging AI is expected to have features such as full-scenario coverage, full-modal integration, full-disease-type understanding, and full-process embedding. However, this goal still requires high-quality data assets, unified standards, a medical imaging base model, a mature AI Agent workflow, and a clearer regulation and payment system. As clinical and economic values are continuously verified, medical imaging AI will gradually move from assisting doctors in completing local tasks to supporting the digitalization of the entire imaging diagnosis and treatment process.
03
Clinical applications of AI medical imaging in various scenarios
Cranial medical imaging: Timeliness and multi-disease diagnosis needs drive AI toward full-process intelligent upgrade:
Cranial medical imaging is widely used in scenarios such as stroke, intracranial hemorrhage, cranial trauma, cerebrovascular diseases, intracranial tumors, and postoperative follow-up. Among them, CT has the advantages of fast examination speed and clinical accessibility, and is an important examination method in emergency and stroke diagnosis; MRI is particularly valuable in the refined assessment of brain tissue, nervous system, and intracranial tumors.
Traditional cranial imaging diagnosis usually requires doctors to observe multiple layers of images within a short time and identify multiple abnormalities such as cerebral hemorrhage, cerebral infarction, space-occupying lesions, edema, and fractures. Some lesion signs are relatively subtle, and there may be overlapping imaging manifestations between different diseases, requiring high professional experience and comprehensive judgment ability of doctors. In emergency and grassroots medical scenarios, insufficient imaging doctor resources and high requirements for report timeliness further increase the diagnostic pressure.
At present, cranial AI medical imaging products have covered multiple disease directions such as cerebral hemorrhage, cerebral infarction, aneurysms, and intracranial tumors, and can assist in lesion identification, localization, segmentation, quantitative analysis, and risk warning. However, most traditional products still focus on single disease types or single tasks, and doctors need to call multiple tools separately and integrate imaging findings and diagnostic conclusions on their own, not fully covering the complete reading and report process of a cranial examination.
The 'White Paper' uses Yinghe Yimai's cranial CT super intelligent "Xiaojun Doctor 2.0" as a case to show the path of cranial AI evolving from single-location lesion detection to inspection item-level intelligent agent. This product takes cranial CT plain scan as the core scenario, based on visual language models, connecting image recognition, anomaly judgment, disease analysis, and report generation. According to the 'White Paper' case, it can cover 94 types of cranial diseases and generate a preliminary report within one minute. Doctors can complete report issuance through the process of "one-click import—AI automatic generation—review and modification". This model is expected to help less experienced doctors obtain standardized references, allow senior doctors to focus more on complex cases, and support departments in carrying out report quality control, teaching, and data preservation.
Thoracic medical imaging: Wide application scenarios, AI moves from single-disease products to multi-anomaly combined analysis
Thoracic imaging has a large examination volume and relatively mature AI product applications. Chest X-ray and CT can be used for screening and diagnosis of diseases such as pulmonary nodules, lung cancer, pneumonia, pulmonary tuberculosis, pulmonary embolism, pleural diseases, mediastinal lesions, and chest wall bone injuries, covering multiple structures such as lungs, airways, mediastinum, pleura, chest wall bones, and large thoracic blood vessels.
The difficulty of thoracic imaging diagnosis lies in the many types of diseases, large amount of image information, and the possibility of multiple abnormalities appearing in a single examination. Doctors need to not only detect lesions but also judge the location, quantity, size, density, and trend of lesions, and integrate multiple imaging findings into a complete report. In medical institutions with a large examination volume, repetitive reading and report writing also increase the burden on doctors.
Currently, chest AI has been widely used in various applications such as lung nodule detection, pneumonia and tuberculosis screening, pulmonary embolism identification, rib fracture detection, quantitative analysis of lesions, risk warning, and follow-up management. Some products have obtained medical device registration certificates, indicating a high degree of product maturity of chest AI. However, different disease-related products remain relatively independent. A single tool usually can only solve some issues in a complete chest examination and cannot replace the doctor's systematic observation of all anatomical structures and various abnormalities.
The "White Paper" introduces the AIR product of Yinghe Medical Pulse, which uses "path-level auxiliary diagnosis" as its core. It connects chest CT image analysis, anatomical structure segmentation, lesion detection, and structured report generation into a continuous process. This product can segment key structures such as the lung area, mediastinum, and pleura, covering more than 19 common lesions including lung nodules, lung tumors, and rib fractures. It presents analysis results through structured reports with graphic and text integration. After doctors review and modify the reports, the feedback can further be used as research and model iteration data, promoting chest AI to evolve from a single-disease detection tool to an intelligent system for complete examination items.
Knee joint medical imaging: Complex anatomy and multi-sequence analysis provide new application opportunities for AI
The knee joint is an important weight-bearing and movement joint in the human body, involving multiple structures such as bones, joint cartilage, menisci, cruciate ligaments, collateral ligaments, synovium, and surrounding soft tissues. Medical imaging is widely used in the diagnosis, treatment planning, and follow-up evaluation of diseases such as knee osteoarthritis, meniscus injury, ligament injury, fracture, bone contusion, synovitis, and joint effusion. Among them, MRI can show tissues such as cartilage, menisci, ligaments, and bone marrow, making it an important imaging method for detailed assessment of knee joint diseases.
Traditional knee joint MRI reading requires doctors to comprehensively observe multiple sequences and judge different anatomical structures layer by layer. Lesions such as meniscus tears, ligament injuries, cartilage degeneration, bone marrow edema, and synovitis may exist simultaneously, and some early or mild abnormalities are difficult to identify. During long-term follow-up, doctors also need to compare changes in cartilage thickness, lesion extent, and fluid accumulation levels, and manual measurement and before-and-after comparisons require significant effort.
Currently, knee joint AI is primarily applied in areas such as anatomical structure segmentation, identification of meniscus and ligament injuries, osteoarthritis assessment, quantitative cartilage analysis, and detection of synovitis and joint effusion. Compared with fields like pulmonary nodules and cerebrovascular diseases, which have higher levels of productization, knee joint AI is still in the stage of transitioning from scientific research verification to clinical application. In the future, with the continuous advancement of multi-sequence information integration, multi-structure combined analysis, and real-world validation, knee joint AI is expected to further support injury grading, treatment plan formulation, post-operative evaluation, and long-term follow-up management.
Other specialized imaging scenarios: Breast and cardiovascular diseases have become important directions for AI productization:
Beyond craniocerebral, thoracic, and knee joint imaging, AI medical imaging is also being rapidly applied in specialized scenarios such as breast and cardiovascular diseases. Breast imaging is mainly used for breast cancer screening, lesion detection, calcification identification, breast density assessment, and auxiliary judgment of benign and malignant conditions. Currently, products for assisting X-ray image detection of breasts have been approved for market release in China, but existing products still mainly focus on single modalities and individual tasks. There is further room for multi-modal combined judgment, risk stratification, structured reports, and follow-up management; cardiovascular imaging AI is mainly used in aspects such as coronary artery stenosis analysis, plaque identification, coronary calcification index, cardiac function assessment, and blood flow functional analysis. In the context of coronary CT angiography, AI can assist in reducing image noise, improving image quality, automatically identifying coronary artery structures and plaques, and performing quantitative analysis of vascular stenosis and calcification levels, thereby reducing the repetitive measurement work of doctors and providing support for coronary heart disease risk assessment and clinical decision-making.
Overall, although AI medical imaging products in different specialties are at different development stages, their evolution direction is consistent: moving from single lesion detection to combined judgment of multiple diseases, from single imaging modality to multi-modal data integration, and from independent algorithm tools to a complete workflow combining imaging analysis, report generation, quality control, and follow-up management.
04
Construction and Prospects of Digital Medicine Imaging Ecosystem
As medical imaging data continues to accumulate, AI capabilities accelerate integration into clinical processes, and regional imaging platforms and information infrastructure continue to improve, the development of digital medicine imaging is shifting from point-based technology application to ecosystem construction. In the future, the medicine imaging ecosystem will no longer operate independently by hospitals, equipment, AI, and information systems. Instead, it will connect imaging acquisition, data flow, intelligent analysis, diagnostic services, patient management, and continuous feedback around real clinical needs, forming an intelligent network with multi-party collaboration and continuous iteration.
The participants in the digital medicine imaging ecosystem are redefining their roles:
Medical imaging service providers are the core application scenarios of the digital medicine imaging ecosystem. Hospitals, medical imaging centers, and health check-up centers are evolving from single inspection and diagnosis nodes to service hubs connecting patient needs, imaging data, professional doctors, and AI capabilities. Through image data interoperability, remote diagnosis, report recognition, referral connection, and continuous follow-up, different institutions can form closer cooperation relationships, promoting medical imaging services from single-institution delivery to regional coordination and full-cycle management.
Medical imaging equipment suppliers are also transforming from hardware providers to ecosystem enablers of "smart devices + AI solutions." In the future, imaging equipment will need to provide high-quality images, as well as capabilities for AI algorithm integration, image quality control, data interfaces, remote operation and maintenance, and continuous upgrading. The integration of equipment, software, computing power, and clinical scenarios will promote imaging equipment to evolve from a simple image acquisition tool to an important entry point for a digital medicine diagnosis system.
AI medical imaging solution providers are shifting from developers of single-point diagnostic tools to builders of underlying capabilities for digital medicine imaging. AI capabilities will further cover pre-examination examination recommendations, scanning optimization and image quality control during examination, as well as lesion identification, quantitative analysis, report generation, and follow-up management after examination. As AI evolves from single-disease algorithms to intelligent agents at the examination item and path levels, solution providers will further connect equipment, data, doctors, medical institutions, and clinical workflows.
Financial technology infrastructure providers will also shift from serving a single hospital to supporting medical alliances, medical communities, and cross-institutional collaboration. In addition to basic systems such as PACS, RIS, HIS, and imaging cloud, information platforms must support data standardization, AI model invocation, diagnostic result transmission, quality supervision, and information security, providing underlying support for the regional imaging model of "base inspection, superior diagnosis, and result sharing".
In this ecosystem, people with imaging examination needs are no longer passive service recipients. With the help of imaging cloud, personal health records, and data authorization mechanisms, patients can more conveniently save, access, and share past imaging data, and achieve continuous use during referrals, reexaminations, and medical treatment in different locations. AI can also assist in interpreting imaging reports, comparing examination results at different times, and provide reexamination and follow-up suggestions, promoting medical imaging from a single diagnostic tool to long-term health management.
At the same time, the Internet + AI medical platform will become a new ecological role connecting online needs and offline imaging services. The platform can identify potential examination needs through AI consultation, risk assessment, and intelligent recommendations, and match services based on the patient's location, examination items, and medical institution capabilities. After examination completion, imaging data and reports can be further connected to specialist consultation, reexamination reminders, and health management, forming a service closed loop of "online demand identification - intelligent resource matching - offline examination completion - online continuous management".
Products, systems, and service capabilities together form the foundation for international exploration:
China's digital medicine imaging ecosystem has already accumulated certain foundations in product commercialization, standardized system construction, and specialized imaging services. In terms of products, AI medical imaging has covered multiple modalities such as X-ray, CT, MRI, and ultrasound, and is gradually integrated into real clinical workflows such as reading reports, report generation, review, and quality control. The industry's evaluation of product value is also shifting from solely focusing on algorithm accuracy to clinical effectiveness, workflow adaptation ability, cross-institution generalization ability, and health economics value. Real-world application results will be an important foundation for the large-scale promotion of products and their entry into overseas markets.
In terms of system construction, China's medical system covers a large population size, multiple medical levels, and significant regional differences, providing complex and rich verification scenarios for the digital medicine imaging model. Around standardized acquisition, data governance, regional imaging centers, remote diagnosis, AI empowerment, quality control, and result recognition, China has gradually formed a relatively systematic regional imaging construction path. The international value of related experience lies not only in exporting single technical products but also in exporting data standards, platform models, quality control rules, and regional collaboration methods.
At the service capability level, imaging cloud, remote reading platforms, AI-assisted diagnosis, and secure transmission technologies enable the sub-specialty experience of imaging doctors, remote consultation capabilities, report review capabilities, and quality control systems to break through geographical limitations. In the future, the imaging service capabilities accumulated in China can be further transformed into standardized service modules such as remote reading, complex case review, night emergency support, professional training, and regional quality control, providing capacity supplementation for overseas markets with relatively limited medical resources.
The international path will gradually move from product export to ecosystem collaborative export:
The internationalization of digital medicine imaging ecosystem usually follows a three-stage path from shallow to deep. The first stage is product export, starting with clinically verified AI medical imaging software, workstations, or examination item-level products, entering local markets through overseas registration, channel cooperation, and hospital pilots. The second stage is data and standards export. On the basis of product implementation, further export of data governance frameworks, structured annotation systems, core definition standards, and quality control methods is carried out to support AI models to be locally adapted to the local disease spectrum, equipment environment, and clinical workflows. The third stage is ecosystem export, namely integrating the platform, AI, and medical imaging services, and building a long-term collaborative mechanism combining remote diagnosis, continuous operation, quality management, and personnel training with local hospitals, imaging centers, equipment providers, and internet medical platforms. As the output content evolves from single products to standards and ecosystem, the implementation difficulty will gradually increase, but the long-term service value and industrial collaboration space will also expand further.
Figure: Digital Medicine Imaging Ecosystem Evolves from Product Export to Ecosystem Collaborative Export
Source: Frost & Sullivan Analysis
05
Main Participants in the Global Digital Medicine Imaging Ecosystem: Multiple Collaborative Models Are Being Accelerated
As medical imaging AI evolves from single-algorithm to clinical workflows and ecosystem applications, major global industry participants are exploring different collaborative paths. The medical imaging service network can provide real clinical scenarios, continuous data sources, and large-scale application entry points, while AI companies provide algorithm products, model development, and platform integration capabilities. Their deep combination is driving AI to gradually transform from an external auxiliary tool to an inherent capability of the imaging service system.
Yinyi Sunshine and Yinghe Medical Pulse: Building a "Scenario - Data - Standard - Model - Product - Feedback" Closed Loop
Yinyi Sunshine and Yinghe Medical Pulse have formed a collaborative model of "medical imaging service network + AI platform". Yinyi Sunshine relies on a network of medical imaging centers covering multiple regions to provide real clinical scenarios, continuously accumulated imaging data, and professional medical service foundation for AI development; Yinghe Medical Pulse focuses on data standard governance, medical imaging base model, examination item-level AI intelligent agent, and clinical application platform, promoting the transformation of data resources into intelligent capabilities.
Different from traditional single-disease and single-task algorithms, Yinghe Medical Pulse pays more attention to using complete examination items as research and application units, connecting image recognition, abnormal judgment, structured reports, quality control, and doctor feedback. Through the Hetu Program, MIIA Medical Imaging Intelligent Agent System, AIR Product System, and MIIA-AI Integration Platform, medical imaging data and doctor experience can be continuously accumulated in real applications and fed back to model training and product optimization, forming a continuous iterative closed loop of "clinical application - data accumulation - standard governance - model training - product implementation - doctor feedback".
Figure: Yinyi Sunshine and Yinghe Medical Pulse Digital Medicine Imaging Ecosystem Closed Loop
Source: Frost & Sullivan Analysis
RadNet and Gleamer: Mature Imaging Service Network Integrated into AI Product Matrix:
RadNet and Gleamer represent the development model of "imaging service network + AI product matrix integration". RadNet has a large-scale outpatient medical imaging center network, covering multiple examination items such as MRI, CT, PET/CT, ultrasound, X-ray, and breast imaging, providing a wide range of clinical deployment scenarios and commercial entry points for AI products.
Gleamer focuses on multi-modal medical imaging AI products, with related products covering X-ray, CT, breast photography, and MRI modalities, capable of assisting in fracture detection, chest abnormality identification, lung analysis, and report generation. By integrating AI products into existing reading and diagnosis workflows, this model can quickly deploy AI capabilities on a large scale relying on a mature imaging service network, improving doctors' reading efficiency and diagnostic consistency.
I-MED and Harrison.ai: Imaging Service Group Supports AI Research and Large-Scale Application:
I-MED and Harrison.ai form a typical model of "imaging service group + AI research platform". I-MED relies on Australia and New Zealand's large-scale medical imaging service network to provide multiple modalities such as X-ray, CT, MRI, ultrasound, breast photography, and nuclear medicine, providing scenario foundation for AI model research, clinical verification, and product deployment.
Harrison.ai and its Annalise.ai focus on the research and development of medical imaging AI products. Related products cover high-frequency examination scenarios such as chest X-ray, cranial CT, and chest CT, emphasizing multi-abnormal joint identification, urgent case alerts, and integration of existing workflows. This model promotes the imaging service network to further become an important foundation for participating in research, verification, deployment, and continuous feedback of AI, enabling AI capabilities to be continuously optimized and promoted in a large-scale clinical network.
Each of the three ecological models has its own focus, and the industry is moving from single-product to systematic capabilities:
From global representative cases, different digital medicine imaging ecological models all focus on the integration of medical imaging services and AI capabilities, but their development focuses differ. RadNet and Gleamer prefer to introduce diverse AI products into a mature imaging service network, with advantages in clinical channels and large-scale commercial implementation; I-MED and Harrison.ai emphasize the joint research and development between the imaging service group and the AI research platform, as well as multi-abnormal detection capabilities; Yinyi Sunshine and Yinghe Medical Pulse further cover real clinical scenarios, data governance, standard construction, base model, examination item-level AI products, platform integration, and doctor feedback iteration, forming a relatively complete digital medicine imaging capability closed loop.
At the same time, enterprises such as global medical imaging equipment providers and imaging service providers are also exploring differentiated AI layouts based on their own resource endowments. Equipment providers mainly start with smart scanning, image reconstruction, workflow optimization, and AI platforms; imaging service providers promote large-scale AI application based on examination scenarios, imaging center networks, and patient access. Cooperation among various entities continues to deepen, promoting the global digital medicine imaging industry to move from single-product competition to ecological competition based on scenario, data, technology, and service collaboration.
