As artificial intelligence moves from identifying individual lesions to understanding complete examination items, as medical imaging data transforms from static storage into trainable, verifiable, and iterative intelligent assets, and as doctors gradually become co-creators of intelligent capabilities through AI tools—the medical imaging industry enters a new stage driven by real-world scenarios, high-quality data, general models, and ecological collaboration.
Recently, Frost & Sullivan released the “2026 Global Digital Medical Imaging Ecosystem Industry White Paper in the Era of AGI” (hereinafter referred to as “The White Paper”). The White Paper systematically outlines the participants, service processes, practical challenges, and policy environment of the medical imaging ecosystem, focusing on the industrial logic of medical imaging AI evolving from single-point algorithms to base models,AI Agentsand examination-item-level intelligence in the AGI era. It also combines clinical scenarios such as brain, chest, and knee joints and global representative ecosystem models to outline the development directions and international pathways of the digital medical imaging ecosystem.
Scan with WeChat to obtain the report
01
Overview of the Medical Imaging Ecosystem: From Dispersed Links to Full-Link Collaboration
Medical imaging is being upgraded from a single examination tool into a comprehensive diagnostic capability:
Medical imaging visualizes human structures, functions, and lesion characteristics using 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 improvement 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 pathway.”
In the future, the medical imaging ecosystem will further form a comprehensive capability system of “scenario + data + AI + platform”: real imaging service scenarios provide clinical entry points for AI, high-quality imaging and report data form the basis for model training and validation, AI enhances key capabilities such as ordering, scanning, diagnosis, and clinical application, and platforms connect data, models, doctors, and medical institutions, enabling different entities to collaborate within a unified workflow.
Multiple parties participate and provide feedback in two directions, forming the digital medical imaging ecosystem:
The digital medical imaging ecosystem is not composed of a single hospital or technology company, but involves people with medical imaging examination needs, hospital and third-party imaging centers as service providers, medical imaging equipment suppliers, AI medical imaging solution providers, and medical informatization infrastructure providers. Equipment providers support image collection, service providers handle examinations and diagnoses, information technology companies ensure data storage, transmission, and interconnection, AI companies provide intelligent capabilities in pre-examination management, in-process collaboration, and post-examination support, and real user feedback from patients and doctors further promotes product optimization.
Image: Digital Medical Imaging Ecosystem and Collaborative Relationships Among Stakeholders
Source: Frost & Sullivan Analysis
Image service quality depends on the overall completion of ordering, scanning, diagnosis, and clinical application:
Medical imaging services are not simply “taking images—getting reports,” but involve a continuous process where clinicians select examination items, technicians perform image collection and quality control, radiologists identify signs and make diagnoses, and clinicians convert results into treatment decisions. Any lack of capability at any node can reduce the final value of the image. For example, inappropriate selection of examination items affects subsequent diagnostic basis, improper image collection limits lesion identification, inaccurate or unclear reports reduce clinical reference value, and incorrect understanding and application of image results by clinicians also affect treatment decisions.
Image capabilities are not limited to a single link but represent a comprehensive capability system throughout the entire medical imaging service process, including clinicians’ decision-making ability, technicians’ image collection and quality control ability, radiologists’ diagnosis and reporting ability, and clinicians’ image application ability. The capabilities of each link are reflected in their completion rate, and together determine the final quality and value of medical imaging services.
Image: Breakdown of Medical Imaging Service Process and Image Capabilities
Source: Frost & Sullivan Analysis
Healthcare institutions at different levels face differentiated challenges, and digitalization needs show clear stratification:
Top-tier tertiary hospitals and regional central hospitals typically have strong equipment and talent foundations. However, they face challenges such as high patient volumes, high image review loads, and increasing pressure from regional quality control and medical consortium management. Their core needs include AI-assisted image review, report efficiency improvement, intelligent quality control, and regional capability delivery. Secondary and tertiary hospitals possess routine examination capabilities, but there is a shortage of high-level radiologists. Complex cases still rely on higher-level hospitals, and their needs focus on auxiliary diagnosis, structured reports, remote verification, and sub-specialty skill enhancement. Grassroots and county-level hospitals encounter issues such as insufficient equipment utilization, shortage of technicians and diagnostic doctors, inconsistent collection and reporting standards, and difficulty in result recognition. They require remote diagnosis, standardized collection, AI preliminary screening, and grassroots quality control capabilities.
Image: Pain points and needs of medical imaging ecosystems in Chinese healthcare institutions at different levels
Source: Frost & Sullivan analysis
Policy directions have shifted from "equipment supplementation and supply expansion" to "strengthening grassroots levels, promoting mutual recognition, establishing standards, and introducing AI":
In recent years, policies related to medical imaging in China have been continuously improved. The management of large medical device allocation 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 consortia, and the "Thousand County Project" promote "grassroots examination, higher-level diagnosis, and result sharing"; guidelines for pricing items of radiological and ultrasound services drive standardization of charging items; the addition of the "AI-assisted" extension indicates that AI is gradually entering standardized clinical applications and payment system discussions.
Policy evolution aligns with industry pain points: Top hospitals need efficiency and regional management capabilities, secondary and tertiary hospitals require enhanced diagnostic skills, while grassroots institutions need to improve basic services and raise mutual recognition levels. The construction of digital medical imaging ecosystems focuses on transitioning from single-point equipment investment to a systematic project involving regional collaboration, process standardization, data governance, talent empowerment, and AI application.
Data standardization and assetization become fundamental outcomes of ecosystem construction:
Medical imaging data features large volume, multiple modalities, significant equipment differences, complex disease types, and inconsistent report expressions. For AI research, unregulated raw images and free-text reports cannot directly convert into high-quality training data. Only through standardized inspection items, structured reports, semantic normalization, professional annotation, and multi-level quality control can scattered data be transformed into trainable, verifiable, and reusable data assets.
02
The logic of AI empowerment in the medical imaging ecosystem
Medical AI has entered the AGI exploration stage, with competition shifting from algorithm accuracy to scenario integration and continuous evolution:
Medical AI has developed through stages such as statistical learning, deep learning, and large language models. Early systems relied on manual rules and feature engineering, while deep learning enabled image recognition, lesion detection, and disease classification for clinical assistance. Large language models significantly improved medical knowledge retrieval, medical record summarization, and text generation efficiency. In the AGI exploration stage, AI gains stronger capabilities in complex reasoning, multimodal perception, task planning, and feedback correction. Its value extends beyond answering questions to enabling real clinical processes.
AI Agents are important application forms in this stage. Models act as "intelligent brains" providing understanding, reasoning, and generation capabilities, while Agents break down tasks, call tools, execute processes, and optimize feedback around specific goals. In medical imaging scenarios, Agents can connect image retrieval, lesion analysis, report generation, quality control, doctor review, and model iteration, transforming AI from external tools into intelligent collaborators in workflows.
Medical imaging AI should be defined by "scenario—role—task" rather than just disease type or algorithm classification:
Medical imaging AI can be divided into four core application scenarios: AI for assisting clinicians in order placement, AI for assisting technicians in scanning and image quality control, AI for assisting radiologists in diagnosis and report generation, and AI for assisting clinicians in diagnosis and decision support. This classification directly corresponds to real service processes, making it easier to evaluate the actual contribution of AI to the rationality of examinations, image quality, image review efficiency, report standardization, and clinical decision-making.
Medical imaging service quality is determined by multiple stages including order placement, image acquisition, diagnostic analysis, and clinical application. By empowering AI across these stages, the accuracy of examination decisions, image acquisition standards, diagnostic efficiency, and clinical value can be further improved, leading to comprehensive improvement of medical imaging capabilities. With all four core capabilities reaching 90%, after AI empowerment, each stage improves to approximately 99%, raising overall imaging capabilities from about 66% to about 97%, a 31 percentage point increase. These 31 percentage points mean fewer missed diagnoses, fewer misdiagnoses, better patient experience, and more efficient resource utilization—this is the core value of comprehensive AI empowerment.
Image: Enhancement of imaging capabilities through comprehensive AI empowerment
Source: Frost & Sullivan analysis
Industry evolution: From AI 1.0 fragmented tools to AI 3.0 ecosystem empowerment platform:
AI medical imaging in phase 1.0 mainly developed models for single organ, single disease, or single task, providing assistance in clear scenarios such as lung nodules, fractures, and bleeding. However, the products were isolated from each other and could not cover the entire image review process. Phase 2.0 began using inspection items and business paths as units, integrating tasks such as detection, segmentation, quantitative analysis, and report generation into scenario-based products, closer to doctors' actual work. Phase 3.0 focuses on medical imagingBase ModelWith the AI-DIY toolchain and integration platform at its core, it promotes mass production, continuous iteration, and ecosystem building of AI capabilities.
Image: The industry evolution of AI medical imaging from 1.0 to 3.0
Source: Frost & Sullivan analysis
The base model for medical imaging becomes an important foundation for general intelligence:
Traditional models follow a "single dataset—single model—single task" development paradigm. Every addition of a disease or task requires re-collecting, labeling, training, and validating data, resulting in long development cycles and high expansion costs. The base model for medical imaging is pre-trained using large-scale, multi-modal images, diagnostic reports, structured labels, and clinical semantics, forming a general understanding of anatomical structures, lesion features, and report languages. It can be quickly extended to downstream tasks such as detection, segmentation, classification, and report generation throughsmall-sample fine-tuningtask adaptation, or tool invocation.
The key barrier to the base model is not just algorithms, but whether companies can establish a closed loop of “real-world scenarios—high-quality data—standard governance—model training—clinical feedback”. Feedback from doctors during image review, report modification, and quality control review can further be transformed into structured training signals, enabling the model to continuously evolve in real applications.
Hetu Plan: Strengthening AI R&D foundations with data standards and professional labeling:
Yinghe Medical Imagingis a platform under Yimai Sunshine focused on the research and application of AI technology for medical imaging. Leveraging Yimai Sunshine’s long-term experience in medical imaging service scenarios, data resources, and professional capabilities, it further develops data governance, model training, and AI application development for medical imaging, promoting the digitalization of the medical imaging service system. Based on this, the Hetu Plan is jointly promoted by Yinghe Medical Imaging, Yimai Sunshine, and partner medical institutions. The Hetu Plan is a systematic data standard construction and labeling plan for the AI 3.0 era in the medical imaging field, and is a representative example of infrastructure construction for medical imaging AI in the industry. Unlike traditional single-point data labeling projects, the Hetu Plan does not focus on a specific disease or model, but starts from industry-level standards, systematically planning the standard semantics, datasets, and model capabilities required for medical imaging AI. Its core goal is to implement “three major infrastructures”: defining standardized semantics for millions of medical images through standard infrastructure, creating high-quality labeled datasets for millions of images through data infrastructure, and building full-modal basic model capabilities through model infrastructure.
Research path in the AI medical imaging 3.0 era: There is no “universal AI”; examination items become the core R&D unit:
Medical imaging involves multiple modalities such as CT, MRI, X-ray, ultrasound, and nuclear medicine. Different examination items have significant differences in scanning methods, anatomical structures, disease spectrum, and clinical purposes. A complete examination usually includes multiple tasks such as anomaly detection, 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 examination items and clinical scenarios.
In the AI medical imaging 1.0 stage, products mainly target a single organ, disease, or task, capable of local lesion recognition, but the application scenarios are relatively fragmented. Starting from the 2.0 stage, complete examination items and business paths are used as units, combining capabilities such as lesion detection, segmentation, quantitative analysis, and report generation. In the 3.0 stage, base models, R&D toolchains, and AI Agents further reduce the R&D barriers for professional AI products, enabling the industry to move from a few single-point product developments to continuous production of AI capabilities at the examination item level.
Ecosystem building becomes inevitable, and doctors transform from AI users to AI creators:
There are numerous examination items and clinical needs covered by medical imaging. Single-tech companies cannot independently understand and meet all niche scenarios. Future R&D models will involve more participation from doctors, medical institutions, and AI platforms: doctors propose clinical questions, define tasks, and set criteria; medical institutions provide real-world scenarios and data feedback; AI platforms provide base models, data governance, labeling, training, validation, and deployment tools.
Under this model, AI Agents need to clearly define specific service targets and responsible entities. Doctors who best understand examination items, diagnostic 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, but through research and model R&D platforms, converts diagnostic experiences, judgment logic, and operational procedures formed by doctors in daily work into structured data and trainable tasks, allowing doctors to participate in data organization, intelligent labeling, model training, and performance verification.
As AI is continuously used in real scenarios, doctors’ image review results, report modifications, and diagnostic feedback can further be transformed into basis for model optimization, turning professional experience into replicable and iterative digital capabilities, achieving “work as training, use as evolution”.
Image: AI-DIY model where doctors participate in the development of medical imaging AI
Source: Frost & Sullivan Analysis
AI Integration Platform Promotes Decentralized Capability into Clinical Workflows:
As the number of medical imaging AI products increases, point-based deployment and independent usage models tend to cause system fragmentation, complex operations, and higher maintenance costs. In the AI Medical Imaging 3.0 era, a unified integration platform is required to connect self-developed and third-party AI capabilities into imaging information systems, and automatically match appropriate tools based on examination items, imaging modalities, and clinical needs.
The AI Integration Platform connects doctors, medical institutions, and AI research achievements on one end, and actual work processes such as image browsing, report editing, quality control, and remote collaboration on the other end. Doctors can utilize AI capabilities within their existing viewing and reporting environments without repeatedly switching between multiple systems. Through unified management, on-demand invocation, and continuous updates, the platform enhances the clinical accessibility of AI products and provides a foundation for business models such as project construction, platform operation, examination services, data services, and AI capability output.
Image: AI Integration Platform connecting research achievements and clinical application scenarios
Source: Frost & Sullivan Analysis
Data-driven continuous iteration forms a closed loop of digital and intelligent imaging capabilities:
Medical imaging AI is not a static product that is trained and delivered once. It requires continuous iteration based on real medical scenarios. Different data in medical imaging services reflect the capabilities of various professional roles: order data reflects the decision-making logic of clinicians regarding examination items, image data indicates equipment imaging and technician skills, diagnostic report data contains the judgment experience of radiologists, and post-processing and clinical application data reflect the practical value of imaging results in treatment decisions, efficacy assessment, and patient management.
By standardizing the management of the above data and continuously feeding feedback from clinical use back to model development, a closed loop of “clinical application—data generation—model optimization—capability upgrade—reapplication” can be established. Thus, AI becomes not just an external auxiliary tool but gradually integrates into the medical imaging service system, promoting the overall improvement of capabilities in ordering, scanning, diagnosis, and clinical application.
Image: Imaging capabilities, data, and AI form a continuous closed loop
Source: Frost & Sullivan Analysis
Prospects for AI Medical Imaging 4.0: Moving towards full scenario, full modality, full disease types, and full process:
On the basis of the continuous enrichment of AI capabilities at the examination item level in the 3.0 era, AI medical imaging will further evolve toward the 4.0 era. Its core lies not in simply increasing the number of models, but in achieving synergy among 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 assessment, and follow-up management.
Future medical imaging AI is expected to feature full scenario coverage, full modality 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, mature AI Agent workflows, and clearer regulatory and payment systems. As clinical and economic values are continuously validated, medical imaging AI will gradually move from assisting doctors in completing local tasks to supporting the digital and intelligent entire imaging diagnosis and treatment process.
03
Clinical applications of AI Medical Imaging in various scenarios
Cerebral Medical Imaging: Timeliness and multi-disease diagnosis needs drive AI’s intelligent upgrade across the entire process:
Cerebral medical imaging is widely used in scenarios such as stroke, intracranial hemorrhage, craniocerebral trauma, cerebrovascular diseases, intracranial tumors, and postoperative follow-ups. Among them, CT has the advantages of fast examination speed and high clinical accessibility, making it an important examination method in emergency and stroke care; MRI is particularly valuable in the detailed assessment of brain tissue, nervous system, and intracranial tumors.
Traditional cerebral imaging diagnosis typically requires doctors to observe multiple layers of images within a short time, while also identifying various abnormalities such as cerebral hemorrhage, cerebral infarction, space-occupying lesions, cerebral edema, and fractures. Some pathological signs are subtle, and there may be overlapping imaging manifestations between different diseases, which requires high professional experience and comprehensive judgment ability from doctors. In emergency and primary healthcare settings, insufficient imaging doctor resources and high requirements for report timeliness further increase the diagnostic burden.
Currently, cranial AI medical imaging products cover various diseases such as cerebral hemorrhage, cerebral infarction, aneurysms, and intracranial tumors. They can assist in lesion identification, localization, segmentation, quantitative analysis, and risk assessment. However, most traditional products focus on single diseases or tasks, requiring doctors to use multiple tools separately and integrate imaging findings and diagnostic conclusions on their own. Thus, they do not fully cover the entire examination and reporting process of a cranial examination.
The 'White Paper' uses the 'Xinye Medical AI Cranial CT Super Intelligent Agent 'Xiaojun Doctor 2.0' as an example to demonstrate how cranial AI evolves from point-based lesion detection to intelligent agents at the examination item level. This product focuses on cranial CT plain scan as its core scenario and connects image recognition, anomaly detection, disease analysis, and report generation based on visual language models. According to the case presented in the white paper, it can address 94 types of cranial diseases and generate a preliminary report within one minute. Doctors can complete the report issuance through a process of 'one-click import—AI automatic generation—review and modification'. This model is expected to help junior doctors obtain standardized references, allowing senior doctors to focus more on complex cases, and also support departmental report quality control, teaching, and data accumulation.
Thoracic medical imaging: wide range of application scenarios, AI moves from single-disease products to multi-anomaly combined analysis
The thoracic area has a large volume of medical imaging examinations and relatively mature AI product applications. Chest X-rays and CT scans can be used for screening and diagnosis of diseases such as pulmonary nodules, lung cancer, pneumonia, tuberculosis, pulmonary embolism, pleural diseases, mediastinal lesions, and chest wall bone injuries, covering various structures including the lungs, airways, mediastinum, pleura, chest wall bones, and major blood vessels in the chest.
The challenge in thoracic imaging diagnosis lies in the many types of diseases, large amount of image information, and the presence of multiple anomalies such as pulmonary nodules, pulmonary inflammation, pleural effusion, emphysema, and rib fractures during a single examination. Doctors need to not only identify lesions but also determine the location, number, size, density, and trend of lesions, and integrate multiple imaging findings into a complete report. In medical institutions with high examination volumes, repetitive reading and report writing also increase the workload of doctors.
Currently, thoracic AI is widely used in nodule detection, pneumonia and tuberculosis screening, pulmonary embolism identification, rib fracture detection, quantitative lesion analysis, risk warning, and follow-up management. Some products have obtained medical device registration certificates, indicating a high degree of productization in thoracic AI. However, different disease products remain relatively independent. A single tool usually only solves some problems in a complete thoracic examination and cannot replace the systematic observation of all anatomical structures and various anomalies by doctors.
The 'White Paper' introduces YUAN CAPITAL’s AIR product for auxiliary diagnosis of thoracic CT plain scan, which focuses on 'pathway-level auxiliary diagnosis' and connects the analysis of thoracic CT images, segmentation of anatomical structures, 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 pulmonary nodules, lung tumors, and rib fractures, and presents the analysis results through structured reports with picture-text integration. After doctors review and modify the report, the feedback can further be accumulated as research and model iteration data, promoting the upgrade of thoracic AI from single-disease detection tools to intelligent agents 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 various structures such as bones, joint cartilage, meniscus, 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, meniscus, ligaments, and bone marrow, making it an important imaging method for detailed evaluation of knee joint diseases.
Traditional knee joint MRI reading requires doctors to observe multiple sequences comprehensively 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 detect. During long-term follow-ups, doctors also need to compare changes such as cartilage thickness, lesion extent, and effusion level, and manual measurement and before-and-after comparisons are labor-intensive.
Current knee joint AI is mainly applied in segmentation of anatomical structures, identification of meniscus and ligament injuries, assessment of osteoarthritis, quantitative analysis of cartilage, detection of synovitis and joint effusion, etc. Compared with fields with higher productization levels such as pulmonary nodules and cerebrovascular diseases, knee joint AI is still in the stage of transition from scientific research verification to clinical application. In the future, with the 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 areas become important directions for AI productization
In addition to cranial, thoracic, and knee joints, AI medical imaging is also being rapidly applied in specialized scenarios such as breast and cardiovascular areas. 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, some breast X-ray image-assisted detection products have been approved for market release in China, but existing products still focus mainly on single modality and single tasks. Multi-modal combined judgment, risk stratification, structured reports, and follow-up management still have room for further development. Cardiovascular imaging AI is mainly used in coronary artery stenosis analysis, plaque identification, coronary calcification index, cardiac function assessment, and hemodynamic analysis. In the context of coronary CT angiography, AI can help reduce image noise, improve image quality, automatically identify coronary artery structure and plaques, and perform quantitative analysis of vascular stenosis and calcification, 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 stages of development, their evolution direction is consistent: moving from single lesion detection to multi-disease combined judgment, from single imaging modality to multi-modal data integration, and from independent algorithm tools to a complete workflow combining image analysis, report generation, quality control, and follow-up management.
04
Construction and Prospects of Digital Intelligence Medical Imaging Ecosystem
As medical imaging data continues to accumulate, AI capabilities integrate rapidly into clinical processes, and regional imaging platforms and information infrastructure improve continuously, the development of digital intelligence medical imaging is moving from point-based technology applications to an ecosystem approach. In the future, the medical imaging ecosystem will no longer operate independently by hospitals, equipment, AI, and information systems. Instead, it will connect image acquisition, data flow, intelligent analysis, diagnostic services, patient management, and continuous feedback around real clinical needs, forming a collaborative and continuously evolving intelligent network.
Participants in the digital intelligence imaging ecosystem are redefining their roles:
Medical imaging service providers are the core application scenarios in the digital intelligence imaging ecosystem. Hospitals, medical imaging centers, and health check-up centers are evolving from isolated examination and diagnosis nodes into service hubs that connect patient needs, imaging data, professional doctors, and AI capabilities. Through shared imaging data, remote diagnosis, report recognition, referral coordination, and continuous follow-up, different institutions can form closer collaboration, 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 into 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 continuous upgrades. The integration of equipment, software, computing power, and clinical scenarios will upgrade imaging equipment from mere image acquisition tools to an important entry point for digital intelligence diagnosis and treatment systems.
AI medical imaging solution providers are shifting from developers of individual diagnostic tools to builders of underlying digital intelligence imaging capabilities. AI capabilities will further cover pre-examination recommendations, scanning optimization and image quality control during examinations, as well as lesion identification, quantitative analysis, report generation, and follow-up management after examinations. As AI evolves from single-disease algorithms to examination item-level and path-level intelligent agents, solution providers will further connect equipment, data, doctors, medical institutions, and clinical workflows.
Healthcare information infrastructure providers are also moving from serving a single hospital to supporting medical consortiums, health communities, and cross-institutional collaboration. In addition to basic systems such as PACS, RIS, HIS, and imaging cloud, information platforms need to support data standardization, AI model invocation, diagnostic result transmission, quality supervision, and information security, providing underlying support for the regional imaging model of "base-level examination, higher-level diagnosis, and result sharing".
In this ecosystem, people with imaging examination needs are no longer passive service recipients. With imaging cloud, personal health records, and data authorization mechanisms, patients can more easily save, access, and share previous imaging data, and achieve continuous use during referrals, reexaminations, and medical treatments in different locations. AI can also assist in interpreting imaging reports, compare examination results at different times, and provide reexamination and follow-up reminders, promoting medical imaging from a single diagnosis tool to long-term health management.
At the same time, the Internet + AI healthcare platform will become a new ecological role that connects online demands with 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 the examination, imaging data and reports can further connect with specialized consultation, reexamination reminders, and health management, forming a service 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 intelligence medical imaging ecosystem has accumulated some foundation in product commercialization, standardized system construction, and professional imaging services. At the product level, AI medical imaging covers various modalities such as X-ray, CT, MRI, and ultrasound, and is gradually integrated into real clinical workflows such as image reading, report generation, review, and quality control. The industry's evaluation of product value is also shifting from simply focusing on algorithm accuracy to clinical effectiveness, workflow adaptability, cross-institution generalization ability, and health economics value. Real-world application results will be an important basis for the large-scale promotion of products and their entry into overseas markets.
At the system construction level, China's medical system has a large population size, multiple levels of medical care, and significant regional differences, providing complex and rich validation scenarios for digital intelligence imaging models. 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 image 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 image 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 collaboration export:
The internationalization of digital intelligence medical imaging ecosystems 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. Based on product implementation, further export data governance frameworks, structured annotation systems, core definition standards, and quality control methods, supporting AI models to be locally adapted to local disease spectra, equipment environments, and clinical workflows. The third stage is ecosystem export, integrating platforms, AI, and medical imaging services, and building long-term collaboration mechanisms combining remote diagnosis, continuous operation, quality management, and personnel training with local hospitals, imaging centers, equipment providers, and internet healthcare platforms. As the output content extends from single products to standards and ecosystems, the implementation difficulty will gradually increase, but the long-term service value and industrial collaboration space will also expand further.
Figure: Digital Intelligence Medical Imaging Ecosystem Evolves from Product Export to Ecosystem Collaboration Export
Source: Frost & Sullivan Analysis
05
Key Players in the Global Digital Intelligence Medical Imaging Ecosystem: Multiple Collaborative Models Are Forming Rapidly
As medical imaging AI evolves from single-point algorithms to clinical workflows and ecosystem applications, major global industry players are exploring different collaboration paths. Medical imaging service networks 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 external auxiliary tools into an inherent capability of imaging service systems.
Yinyi Sunshine and Yinghe Medical Pulse: Building a "Scenario—Data—Standard—Model—Product—Feedback" Loop
Yinyi Sunshine and Yinghe Medical Pulse have formed a collaborative model of "medical imaging service network + medical imaging AI platform". Yinyi Sunshine relies on a multi-regional medical imaging center network to provide real clinical scenarios, continuously accumulated imaging data, and professional medical services for AI research. Yinghe Medical Pulse focuses on data standard governance, medical imaging base models, examination item-level AI agents, and clinical application platforms, converting data resources into intelligent capabilities.
Different from traditional single-disease and single-task algorithms, Yinghe Medical Pulse emphasizes 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 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 loop of "clinical application—data accumulation—standard governance—model training—product implementation—doctor feedback".
Figure: Yinyi Sunshine and Yinghe Medical Pulse Digital Intelligence Imaging Ecosystem Loop
Source: Frost & Sullivan Analysis
RadNet and Gleamer: Mature Imaging Service Networks 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 various examination items such as MRI, CT, PET/CT, ultrasound, X-ray, and breast imaging, providing extensive 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 imaging, and MRI modalities, assisting in tasks such as fracture detection, chest abnormality identification, lung analysis, and report generation. By integrating AI products into existing image reading and diagnosis workflows, this model can quickly implement large-scale AI deployment through mature imaging service networks, improving doctors' image 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 have formed a typical model of "imaging service group + AI research platform". I-MED relies on large-scale medical imaging service networks in Australia and New Zealand, providing multi-modal services such as X-ray, CT, MRI, ultrasound, breast imaging, and nuclear medicine, providing scenario foundations for AI model research, clinical verification, and product deployment.
Harrison.ai and its Annalise.ai focus on developing medical imaging AI products, with related products covering high-frequency examination scenarios such as chest X-ray, head CT, and chest CT, emphasizing multi-abnormal joint identification, critical case alerts, and existing workflow integration. This model transforms the imaging service network from a user of AI products into an important foundation for participation in research, verification, deployment, and continuous feedback, enabling AI capabilities to be continuously optimized and promoted in large-scale clinical networks.
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 intelligence imaging ecosystem models all focus on the integration of medical imaging services and AI capabilities, but their development priorities differ. RadNet and Gleamer prefer introducing diverse AI products into mature imaging service networks, with advantages in clinical channels and large-scale commercial implementation. I-MED and Harrison.ai emphasize joint research and multi-abnormal detection capabilities between imaging service groups and AI research platforms. Yinyi Sunshine and Yinghe Medical Pulse further cover real clinical scenarios, data governance, standard construction, base models, examination item-level AI products, platform integration, and doctor feedback iteration, forming a relatively complete digital intelligence imaging capability loop.
At the same time, global medical imaging equipment providers, imaging service providers, and other enterprises are also exploring differentiated AI layouts based on their resource endowments. Equipment providers mainly start with smart scanning, image reconstruction, workflow optimization, and AI platforms. Imaging service providers promote large-scale AI application through examination scenarios, imaging center networks, and patient access. Cooperation among various entities continues to deepen, driving the global digital intelligence medical imaging industry from single-product competition to an ecosystem competition based on scenarios, data, technology, and services.
Recommended Reading for This Article
↓↓Scan the QR code below on WeChat to obtain the report↓↓
About Frost & Sullivan Medical Health Division
Frost & Sullivan Medical Health Division has professional analysis capabilities and rich project experience in the medical health field. Leveraging Frost & Sullivan's global think tank resources and cross-industry business development platform in Greater China, the Medical Health Division has unique core advantages in investment and financing services for the life and health industry. The Medical Health Division has a wide range of corporate clients in China and has established a large customer network over the past 28 years, accumulating extensive project experience in various segments of life sciences.
Project types include knowledge center services (in-depth content promotion, brand event promotion), international strategy services (go-to-market strategies, industry assessments, global brand promotion, value assessment of go-to-market pipelines), Pre-IPO services (DCF valuation, business plan services), IPO listing services (industry consulting, clinical audit, fundraising writing), market research and strategic consulting, etc. It cooperates with well-known domestic and international information platforms and investment and financing institutions to provide one-stop solutions in the specialized fields of life sciences for enterprises, attracting widespread attention from investors.
