Conversation with Professor Dinggang Shen: A Pioneer Honored with MICCAI's 2025 Enduring Impact Award
Dinggang Shen, Founding Dean of the School of Biomedical Engineering at ShanghaiTech University and Co-CEO of United Imaging Healthcare , was recently honored with the 2025 Enduring Impact Award (EIA) by the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, a leading global academic organization in the field. He is the first Chinese recipient since the award was established 17 years ago.
Professor Shen was also named a MICCAI Fellow in 2020, making him the first Chinese scientist to receive this distinction. Now, with the society’s highest honor, he has once again made history. Over the past two decades, he has dedicated his academic career to advancing the application of artificial intelligence in medical imaging.
At MICCAI 2025, VCBeat spoke with Professor Shen about his research journey, the evolution of AI in medicine, and his vision for cultivating Chinese medical AI talent through deeper collaboration among industry, academia, research, and clinical practice.
The Farsighted “Minority”
Like many pioneers who venture into the unknown, Professor Shen was once part of the “minority.” While most researchers followed conventional paths, he chose to stand apart from prevailing trends. With vision and perseverance, he carved out a path that would later prove transformative.
He was among the earliest scientists in the world to conduct research on AI in medical imaging and one of the first to apply deep learning to the field. More than 20 years ago, he published the seminal paper HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration, which used machine learning methods to analyze MRI data and predict the risk of Alzheimer’s disease. This work was the first study to apply machine learning to brain imaging. The HAMMER elastic registration algorithm introduced in that paper remains a classic method for standardized brain image analysis today.
At the time, however, “machine learning” was far from mainstream in medical image computing. Many in the field saw it as “niche” and “impractical.” Yet, guided by academic intuition, Shen persistently made it the core methodology of his daily research. With the gradual evolution of artificial intelligence technology, he again pioneered the application of deep learning techniques to medical image analysis as early as 2013.
To build a solid foundation for his research, Professor Shen diligently focused on two key tasks: on one hand, he continuously deepened his research efforts, publishing a series of high-quality papers; on the other hand, he meticulously refined his research proposals, often spending weeks polishing a single-page research statement. He openly shared that the proposal-writing discipline he developed is something he has since passed on to students and colleagues.
If embracing machine learning was a bold academic experiment, then transitioning from lab research to industry was an even greater leap. From the beginning, Shen believed that the most advanced scientific discoveries must ultimately be translated into real-world applications that benefit patients. By 2010, improvements in computing power, the explosion of clinical data, and breakthroughs in neural network training had made the transition of medical AI from lab to industry both feasible and necessary.
He also observed that academia often abstracts complex clinical problems into simplified research models whereas real-world challenges are rarely idealistic or singular. This disconnection makes many academic methods difficult to apply in hospitals. Shen concluded that only by bridging academia and industry could AI achieve a full loop from technology to product and truly address clinical needs.
In 2017, convinced that medical AI had reached a “technological tipping point”, Shen co-founded United Imaging Intelligence Co., Ltd. (UII) after repeated invitations from Shanghai United Imaging Healthcare marking the start of his journey to industrialize medical AI.
Today, after eight years of growth, UII is one of China’s largest medical AI companies, with a valuation exceeding RMB 10 billion. Under Shen’s leadership, the company has developed over 100 AI applications, launched the uAI NEXUS medical large model, introduced more than ten medical AI agents, and deployed products in more than 4,000 hospitals worldwide benefiting countless patients.
Building an Innovation Consortium: Industry, Academia, Research, and Medicine
Beyond clinical applications, Shen has focused on solving structural challenges in the medical AI ecosystem, such as talent shortages and disconnections between research and practice. To this end, he founded the School of Biomedical Engineering at ShanghaiTech University, where he serves as Founding Dean.
He highlights the scarcity of interdisciplinary talent as a pressing issue. Many computer science professionals working in medical imaging lack a deep understanding of imaging equipment principles or clinical diagnostic logic, leading to products that often fall short of real clinical needs.
Shen’s solution is deep university–enterprise collaboration. Universities often lack real-world data and clinical scenarios, while companies can provide engineering capabilities, clinical access, and large-scale datasets. By integrating these resources, students not only learn theoretical AI knowledge but also gain early exposure to clinical environments, preparing them to enter both hospitals and industry with practical skills.
His talent development philosophy follows a “Problem-Driven Approach + Global Perspective.” Industrial challenges are reframed as scientific questions. For example, the inherent conflict between “fast scanning and low dose” can become a research topic at ShanghaiTech, where students refine algorithms with real-world data and hospital feedback. Many of these students end up solving problems even companies struggle with, gaining both technical expertise and a sense of achievement.
Professor Shen has long worked to connect Chinese students and scholars with global expertise. In 2012, with Professor Tianming Liu of the University of Georgia, he launched the “Dragon Star Committee”, bringing leading international experts back to China to teach undergraduates. In 2014, he spearheaded the Medical Imaging and Computing Summer School (MICS), which grew from 100 participants at its first session to nearly 3,000 by 2025, making it China’s largest and most influential conference in the field. In 2019, as Conference Chair of MICCAI, Shen helped give Chinese scholars global visibility.
These initiatives have yielded remarkable results: the proportion of MICCAI papers from Chinese scholars has risen from just 2–3% two decades ago to 48.7% in 2025, ranking first globally (compared to 11.5% from the U.S. and 6.4% from Germany).
Engaging Physicians in AI Innovation
Having built a pipeline for talent, Shen sees the next challenge as attracting more physicians into medical AI innovation in completing the industry–academia–research–clinic ecosystem. Currently, the core obstacles physicians face when participating in AI R&D lie in the difficulty of accessing clinical data and meeting clinical needs. In other words, it is necessary to answer the question: Why would physicians be willing to collaborate with enterprises?
For Professor Shen, the solution is to involve physicians directly in the innovation process.
"Currently, United Imaging Intelligence (UII) undertakes over 80 major national and provincial-level projects. Through in-depth industry-clinical collaboration, we obtain relevant medical data to address the challenge of data inaccessibility. All this data has undergone professional annotation and verification, enabling rational utilization within the scope of compliance requirements. Additionally, physicians are deeply involved in the product R&D process—this allows our engineers to promptly respond to physicians' clinical needs and continuously optimize products to better align with their usage habits," Professor Shen told VCBeat.
"We have collaborated with physicians from top-tier hospitals such as West China Hospital of Sichuan University and Zhongshan Hospital in Shanghai to publish a series of high-quality academic papers. This has successfully established a complete closed loop from cutting-edge scientific research to clinical translation and application, enabling medical AI to serve in clinical diagnosis and treatment practices and benefit more people."
Original article at VCBeat: