# Medical AI Ecosystem Innovation Forum and iMedLoop Global Medical Imaging Data Platform Launch Held in Beijing

- Link: https://www.thailand-business-news.com/pr-news/medical-ai-ecosystem-innovation-forum-and-imedloop-global-medical-imaging-data-platform-launch-held-in-beijing
- Published: 2026-07-10T16:04:00+07:00
- Author: PR Newswire

**When Healthcare Meets AI: A New Era of Ecosystem-wide Innovation Is Accelerating**

BEIJING, July 10, 2026 /PRNewswire/ — On July 4, the Medical AI Ecosystem Innovation
Forum and iMedLoop Global Medical Imaging Data Platform Launch was held in Beijing.

Jointly organized by Liaowang Finance, under _Liaowang Weekly_, and Diagens Technology,
the event was guided by the theme of "AI for Science", bringing together stakeholders
from government, industry, academia, research, and healthcare across the medical
AI ecosystem. More than 100 representatives attended the forum, including experts
and leaders from the Chinese Academy of Sciences, the Chinese Academy of Engineering,
the China National Health Association, the China Academy of Information and Communications
Technology (CAICT), the Cyberspace Administration of Zhejiang Province, Zhejiang
Cancer Hospital, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School
of Medicine, Hangzhou Data Group, and Legend Holdings.

During the event, Diagens Technology officially launched the iMedLoop Global Medical
Imaging Data Platform, a proprietary platform developed specifically for the medical
AI industry. More than 30 strategic cooperation agreements were also signed. Together,
these initiatives establish a practical platform for collaboration among government,
industry, academia, research, and healthcare sectors to advance the high-quality
development of medical AI, while providing infrastructure to support China’s participation
in the global medical AI ecosystem.

**Unlocking the Full Value of Data for Medical AI**

As the digital economy converges with the Healthy China strategy, artificial intelligence
has become a key driver of new-quality productive forces in healthcare. With the
continued advancement of tiered diagnosis and treatment, precision medicine, and
smart hospitals, medical imaging has become an essential foundation for disease 
screening, clinical diagnosis, and medical research. As a result, the value of medical
imaging data continues to grow, making compliant data circulation and utilization
an inevitable direction for industry development. China’s National Data Administration,
in its Action Plan for the Development of Trusted Data Spaces (2024–2028), has explicitly
identified healthcare as one of the priority sectors for the development of trusted
data spaces.

During the keynote session, Professor Chen Runsheng, bioinformatician and researcher
at the Institute of Biophysics, Chinese Academy of Sciences, delivered a presentation
entitled "Technical Principles and Future Challenges of Large AI Models." He explained
the technological foundations, innovative nature, and future prospects of large 
AI models, noting that artificial intelligence has become deeply integrated into
medical imaging and is increasingly serving as an essential analytical tool. He 
remarked that AI can integrate the knowledge and expertise of medical imaging specialists,
bringing together multiple analytical approaches to deliver high-quality imaging
analysis capabilities. In his view, AI’s greatest strength lies not only in processing
vast volumes of imaging data, but also in consolidating the knowledge and experience
of multiple experts, overcoming the limitations of individual interpretation in 
ways that traditional manual image reading cannot achieve.

Drawing on frontline experience in hospital digital and smart transformation, Cai
Xiujun, Academician of the Chinese Academy of Sciences and President of Sir Run 
Run Shaw Hospital, vividly demonstrated the innovative applications of AI in healthcare.
Through practical examples — including remote robotic surgery, remote ultrasound
diagnosis, intelligent pre-consultation systems, and AI-assisted medical imaging
diagnosis — he demonstrated the innovative applications of AI in healthcare. Academician
Cai emphasized that the core value of medical AI lies in solving real clinical challenges,
improving the capabilities of primary healthcare institutions, and continuously 
enhancing patient experience. He also identified data quality, data scale, and data
security as the three critical factors determining the success of AI applications
in healthcare. Poorly standardized or low-quality data, he noted, directly reduces
AI performance and ultimately limits its clinical value and broader adoption. He
called on the industry to prioritize standardized medical data governance and robust
compliance and security frameworks as the foundation for AI-enabled healthcare.

Academician Dong Jiahong of the Chinese Academy of Engineering, Dean of the School
of Clinical Medicine at Tsinghua University and President of Beijing Tsinghua Changgung
Hospital, stated that the engineering foundations for AI hospitals are now in place,
driven by the simultaneous maturation of three pillars: the commercialization of
AI-powered medical devices, the advancement of large medical AI models to near-specialist
levels of clinical reasoning, and the engineering development of AI agents. Unlike
smart hospitals, internet hospitals, or medical alliances, AI hospitals, he explained,
are built upon digital twins and powered by AI-native operational logic. Such hospitals
fundamentally reshape the entire healthcare workflow — from perception and cognition
to decision-making and service delivery — enabling seamless integration of online
and offline healthcare while providing proactive, lifecycle-wide health management
that truly realizes the vision of AI Healthcare.

Dou Xizhao, President of the China National Health Association, observed that medical
AI is rapidly evolving from isolated product applications toward comprehensive, 
ecosystem-driven development. Industry competition, he said, is no longer defined
solely by algorithms and models, but increasingly by data resources, standards, 
application scenarios, innovation ecosystems, and integrated service capabilities.
He emphasized that medical imaging data, given its scale, value, and broad applicability,
provides a critical foundation for AI-assisted diagnosis and medical research innovation.
He called on all stakeholders to strengthen open collaboration and, under the principles
of legal compliance and data security, fully unlock the value of medical data so
that it can better serve medical research, clinical practice, and industrial innovation.

**Building a Trusted Industrial Foundation for Medical Imaging AI**

Building a trusted collaborative platform that spans the entire medical imaging 
data lifecycle is fundamental for the industry to overcome development bottlenecks
and achieve large-scale adoption.

[⌊Dr. Song Ning (fourth from right), Chairman of the Board and Chief Executive Officer
of Diagens Technology, Mr. Tang Yaoguo (fifth from right), Chairman of Liaowang 
Times Media and Liaowang Finance, and other distinguished guests jointly witness
the launch of the iMedLoop Global Medical Imaging Data Platform.⌉⌊Dr. Song Ning (
fourth from right), Chairman of the Board and Chief Executive Officer of Diagens
Technology, Mr. Tang Yaoguo (fifth from right), Chairman of Liaowang Times Media
and Liaowang Finance, and other distinguished guests jointly witness the launch 
of the iMedLoop Global Medical Imaging Data Platform.⌉[
Dr. Song Ning (fourth from
right), Chairman of the Board and Chief Executive Officer of Diagens Technology,
Mr. Tang Yaoguo (fifth from right), Chairman of Liaowang Times Media and Liaowang
Finance, and other distinguished guests jointly witness the launch of the iMedLoop
Global Medical Imaging Data Platform.

At the event, Dr. Song Ning, Chairman of the Board and Chief Executive Officer of
Diagens Technology, officially unveiled the iMedLoop Global Medical Imaging Data
Platform. He noted that there are more than 3,000 medical imaging indications worldwide,
while traditional AI model training typically requires hundreds of thousands of 
annotated images, with each annotation taking approximately one hour. Even if hundreds
of thousands of imaging, pathology, and laboratory professionals across China devoted
one hour per day to annotation work, completing the annotations for all projects
would still take more than a thousand years.

To address the industry’s heavy reliance on annotated data for model training, Diagens
Technology launched iMedImage®, the world’s largest medical imaging foundation model
by parameter scale in its field, in May 2025. According to Dr. Song, the foundation
model reduces the amount of annotated data required for disease-specific model training
to one two-hundredth of traditional levels, shortens development cycles to one-twelfth,
and reduces both development costs and computing expenses to one-tenth. Leveraging
this foundation model, Diagens has participated in six national and provincial-level
major projects and collaborated with 87 leading hospitals over the past 12 months
to train 145 vertical AI models.

Regarding data annotation, Dr. Song identified four major pain points in current
global annotation tools: inconsistent data formats that are difficult to process,
low manual annotation efficiency, uneven annotation accuracy, and challenges in 
multi-person collaboration and quality control. To address these issues, Diagens
introduced iMedStudio, a new-generation intelligent annotation tool featuring four
core capabilities: multimodal integration, human-AI collaboration, precise segmentation,
and intelligent arbitration.

iMedLoop integrates the iMedImage® foundation model, the iMedStudio intelligent 
annotation tool, and the iMedMaaS online model training and deployment platform 
to create a closed-loop ecosystem for medical data annotation and circulation, vertical
model training, and model deployment. The platform is now officially open, with 
more than 3,000 professional annotators onboarded, 28.95 million high-quality data
records and over 100 medical AI models deployed, and active participation from multiple
data suppliers, AI healthcare companies, and ecosystem partners, establishing a 
strong resource and industrial foundation for the global medical AI industry.

Dr. Song stated that the platform is committed to deep integration of technology,
data, and application scenarios, and that through the joint efforts of hospitals,
research institutions, and technology companies, China’s medical AI industry has
the potential to become a new pillar of the global healthcare sector.

**Collaboratively Building an Innovative Medical AI Ecosystem**

The high-quality development of medical AI requires coordinated efforts from diverse
stakeholders. A roundtable discussion was held during the forum, bringing together
representatives from basic research, clinical practice, policy and standards, and
platform operations to discuss the construction of an industry-wide innovation ecosystem.

Academician Zhan Qimin of the Chinese Academy of Engineering, Director of the National
Institute of Health Data Science at Peking University, stated that AI is driving
oncology toward more personalized and precise treatment. "In the past, treatments
were often broad and one-size-fits-all, without sufficient consideration for individual
differences and precision. Such approaches could lead to significant side effects
and limited efficacy. Today, by combining multi-omics data with AI analysis and 
applying the insights to pathology slides, it is becoming possible to provide each
cancer patient with a truly tailored treatment plan." He also highlighted AI’s potential
in drug discovery, including shorter development cycles, lower costs, and higher
success rates. In his view, the integration of AI and medicine is shortening the
distance between the laboratory and the clinic, providing sustained momentum for
the evolution of the medical AI ecosystem.

Zhang Hong, Deputy Party Secretary and Executive President of Zhejiang Cancer Hospital,
emphasized the importance of real-world clinical application scenarios within ecosystem
collaboration. He argued that for AI to be adopted in hospitals, it must meet three
requirements: improved efficiency, ease of use, and data security. "All three standards
are indispensable." Clinical practice, he said, is both the ultimate testing ground
for AI value and the source of feedback that drives technological iteration. Only
when hospitals can afford to use AI and use it effectively can AI complete the value
loop from research to application.

Ren Jiuxuan, Deputy Director of the Digital Health Department at the Institute of
Cloud Computing and Digitalization of the China Academy of Information and Communications
Technology, stated that the healthy development of the medical AI ecosystem depends
on a unified evaluation framework. "We are building a dual-track evaluation system
covering both laboratory testing and clinical validation. In addition to general
model capability assessments, we have introduced testing for AI agents capable of
multi-turn dialogue, because real clinical diagnosis is an interactive process in
which patients and doctors gradually uncover information together rather than providing
all information to AI at once." He noted that China has advantages in data resources
and application scenarios, that the diversity of domestic AI products already exceeds
that of the United States, and that the computing gap between the two countries 
is narrowing. He believes that high-quality medical datasets will experience explosive
industry growth within the next one to two years.

From the perspective of industrial practice, Dr. Song Ning explained the technological
foundation of ecosystem collaboration. "iMedImage® is the technological foundation;
without it, building an ecosystem would be like building a castle on sand. iMedLoop
is the collaborative platform that integrates annotation, governance, validation,
and the entire workflow." He emphasized that the platform will remain open and work
with medical institutions, research organizations, and industry partners to lower
the barriers to AI-driven healthcare innovation. "The greatest challenge remains
technological breakthroughs. Once the underlying technology advances, regulation
and commercialization will gradually follow. What is required is long-term commitment."

Zheng Mingzhi, former Vice Chairman of the Zhejiang Federation of Industry and Commerce
and Vice President of the Zhejiang Merchants Development Institute, remarked that
the future of medicine belongs not only to those who understand AI, but also to 
those who can apply AI appropriately. He stressed that the healthcare industry must
keep pace with the AI era and make AI a true clinical support tool and assistant
for physicians. In this process, he said, the iMedLoop platform is poised to play
a very important role.

A strategic cooperation signing ceremony for the co-development of the medical AI
ecosystem was also held during the forum. Hangzhou Data Group, Legend Holdings, 
the Wenzhou Municipal Health Commission, Zhengzhou People’s Hospital, the School
of Mathematics, Physics and Medicine of Zhejiang Normal University, InferVision,
and dozens of other institutions reached cooperation agreements. Leveraging the 
iMedLoop platform, the parties will collaborate on data governance, algorithm innovation,
model development, and clinical validation to build a comprehensive medical AI innovation
ecosystem, explore new pathways for improving healthcare delivery, and contribute
to the advancement of the Healthy China initiative.

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