Research Update
08
Jan
On the afternoon of January 4, 2026, the second TANSI Weekly Forum and the sixteenth session of the “AI Governance Salon”, focusing on smart healthcare governance, was successfully held. The event was hosted by the National Academy of Development and Strategy, Renmin University of China, and organized by the RUC Institute for AI Governance, the School of Interdisciplinary Studies, the School of Population and Health, and the Interdisciplinary Center for New Era Smart Governance.
The seminar invited Professor GONG Xinqi, Professor and Vice Dean of the School of Interdisciplinary Studies and Director of the National Innovation Platform for Big Data and Artificial Intelligence in Governance, and Professor LIANG Hailun, Professor and Vice Dean of the School of Population and Health, to deliver keynote speeches.
The discussion session featured contributions from Professor SONG Yueping, Party Secretary of the School of Population and Health; Professor ZHU Yicheng, Chief Neurologist at Peking Union Medical College Hospital; Professor DA Yuwei, Director of the Neuromuscular Disease Department at Xuanwu Hospital, Capital Medical University; Professor ZHAO Luqing, Deputy Director of the Digestive Center at Beijing Hospital of Traditional Chinese Medicine, Capital Medical University; Professor ZHANG Shengsheng, Director of the Digestive Center at Beijing Hospital of Traditional Chinese Medicine, Capital Medical University; and JIANG Weilin, Director of the Comprehensive Division of the Primary Department, National Health Commission.
LIN Shangli, former President of Renmin University of China and Professor at the School of International Studies, delivered the concluding remarks and summary. The event was attended by over 70 faculty members and students from Renmin University and other institutions, with Professor LIU Wei, Director of the Institute for AI Governance, serving as the chair.
Professor GONG Xinqi delivered a report titled “Smart Healthcare Computing”. He noted that the development of smart healthcare computing is driven by the dual forces of the “Healthy China” strategy and the “AI+” policy, forming a coordinated progression of “policy guidance-technological breakthroughs-practical implementation”.
On the one hand, at the national level, policies are clarifying the implementation pathways for AI in medical management, primary public health, and the health industry, giving rise to a series of AI health assistants that support both community services and personal health management. On the other hand, over the past three years, foundational research in AI for healthcare has rapidly evolved in areas such as Transformers, Diffusion models, pre-trained models, and intelligent agents, providing new methods for tasks like molecular interaction prediction and protein structure analysis.
Building on this, the Mathematical Intelligence Application Laboratory at Renmin University of China has conducted full-chain research ranging from clinical assistance to molecular design, with a focus on antibody drug screening and optimization and the development of the “Healthy China Intelligent Agent”. This work demonstrates the deep integration and systematic empowerment of AI technology, linking macro-level policy frameworks with micro-level medical research.
Professor LIANG Hailun delivered a report titled “Data-Intelligent Approaches to Healthy Aging”. She stated that from an interdisciplinary perspective, the pathways for smart health governance can be systematically explored, with a particular focus on cognitive health issues in the context of healthy aging. She highlighted that neuropsychiatric symptoms are important early indicators of cognitive decline and, through empirical studies, demonstrated the associations between socioeconomic status and symptoms such as depression and anxiety, as well as the mechanisms by which these associations can be modulated through social engagement.
Building on this, she and her team innovatively proposed and developed an adversarial intra-modal imputation method to address missing multimodal data, improving the early detection accuracy of depression. They further constructed a proactive health intelligent agent framework, supported by big data and AI, which integrates multi-source heterogeneous data to advance cognitive health management for older adults toward more precise, dynamic, and personalized approaches.
At the same time, through interdisciplinary courses, data platform development, and the integration of industry, education, and research, her team is committed to training interdisciplinary talent and promoting paradigm innovation and practical applications in the “AI + health” domain.
During the discussion and evaluation session, experts and scholars from various fields shared insights on smart healthcare governance, drawing on their research and practical experience.
Professor SONG Yueping noted that medical AI requires a transformation in regulatory logic, shifting from static approval processes to dynamic forms of oversight. The use of intelligent agents places physicians in dual roles as both principals and agents, making it necessary to design corresponding mechanisms to clarify responsibility and risk. In addition, current AI models rely heavily on data from large hospitals, which may give rise to algorithmic bias and result in insufficient applicability in primary care settings and in remote or underserved regions.
Professor ZHU Yicheng argued that discussions of smart healthcare must distinguish among three application levels with differing risk profiles: the individual, the population, and the policy level. With regard to current wearable devices, the core issue of application lies in clearly defining “what data are to be collected,” a question that is not only technical in terms of sensor design but also a critical consideration for subsequent commercialization and practical deployment. Moreover, as data constitute a key strategic asset, the issue of data ownership is one that must be clearly addressed in the future governance of smart healthcare.
Professor DA Yuwei observed that, from a clinical perspective, although AI-assisted diagnostic tools have already become embedded in everyday medical practice, their outputs are often derived primarily from guideline-based literature and therefore struggle to address complex real-world cases. Physicians must thus maintain cautious and independent clinical judgment. She further emphasized that research on rare neurological diseases faces the fundamental challenge of data scarcity; this stands in tension with existing AI paradigms that depend on large-scale datasets. As a result, there is an urgent need to develop AI algorithms capable of delivering precise diagnosis and treatment on the basis of small-data settings.
Professor ZHAO Luqing argued that the integration of traditional Chinese medicine and artificial intelligence faces inherent tensions. The holistic and dynamically differentiated approach of traditional Chinese medicine is difficult to reconcile with the standardized algorithms employed by AI systems. Key diagnostic data, such as those derived from tongue and pulse examination, lack unified standards and are of uneven quality. In addition, challenges remain in the form of gaps in collaboration between medical and engineering communities, as well as insufficient patient trust. To overcome these bottlenecks, she proposed establishing standardized traditional Chinese medicine databases and developing interpretable AI models tailored to the characteristics of traditional Chinese medicine. At the implementation level, she suggested promoting an “AI-based preliminary screening plus expert review” model and optimizing age-friendly design in order to enhance the credibility and accessibility of services.
Professor ZHANG Shengsheng argued that the digital empowerment of the healthcare sector should span the entire continuum of care, including disease prediction, diagnosis, treatment, and rehabilitation. At present, the core challenge lies in breaking down data silos between hospitals and achieving the integration and effective utilization of high-quality clinical data. He further proposed that the “metaphysical” and rational mode of pattern differentiation in traditional Chinese medicine may possess a distinctive affinity with algorithm-based digital models, potentially enabling syndrome analysis to support diagnostic reasoning and therapeutic decision-making.
Director JIANG Weilin noted that, from the perspectives of policy-making and industry regulation, the governance of smart healthcare must address three core issues. First, it is necessary to establish unified standards and application norms in order to promote the integration of data platforms. Second, there is a need to explore long-term, sustainable operational models and funding mechanisms. Third, empirical research should be conducted to verify whether AI truly reduces costs and improves health outcomes. He further emphasized that the deployment of AI in the healthcare sector must strike a balance among equity, authenticity, and clear accountability.
During the Q&A and discussion session, the on-site speakers responded to questions raised by the audience.
Professor LIN Shangli delivered the concluding remarks. He pointed out that AI applications in the current medical field remain in the “shallow end”, and cannot yet fully replace physicians in diagnosis; the ultimate responsibility for medical care still lies with humans. In light of this reality, the “Healthy China” strategy should not passively wait for technology to mature. Instead, AI research and application should be proactively integrated into top-level design to build a forward-looking smart healthcare service system.
The benefits of AI should focus on ensuring physiological “integrity” and life “health”, laying the foundation for higher-level well-being, with development pathways that are clearly structurally differentiated. This process will profoundly promote the transformation of medical education toward interdisciplinary integration, and accelerate the formation of a comprehensive “Big Health” system encompassing the four dimensions of individual, protection, lifestyle, and ecology.
At the same time, digital technologies will inevitably drive a full-chain restructuring of medical organizations, governance, and responsibility systems, requiring proactive research and shaping of this transformation. Crucially, in an era dominated by rapidly advancing technology, the state, society, and individuals must remain highly conscious, actively steering technology rather than being subjugated by it, and always defending human agency in the pursuit of health. Only by maintaining this clarity and initiative can we confidently move toward a human-centered health future in a digital era full of limitless possibilities.
Proofreaders: WANG Wei, LIU Wei
Translator: ZHANG Yuqing
Web Editor: ZHANG Jingjing
Introduction to the Innovation Hub
The Institute for AI Governance at Renmin University of China was established on April 25, 2025, as a university-level innovation hub built upon the Institute for Interdisciplinary Science. The Institute aims to fully leverage the university’s multidisciplinary strengths across the humanities, social sciences, and science and engineering, and to establish an interdisciplinary collaborative mechanism. Focusing on frontier research, platform development, talent cultivation, and policy consultation, the Institute is committed to producing first-class outcomes in the field of AI governance that combine academic leadership with practical value. It seeks to develop an AI governance paradigm that balances technological frontiers with humanistic values, actively participate in the reform of the global governance system, and serve the country’s long-term development in the field of artificial intelligence.
About TANSI Weekly Forum
“TANSI Weekly Forum” is a cutting-edge interdisciplinary discussion platform meticulously developed by the National Academy of Development and Strategy at Renmin University of China. It focuses on academic and policy frontier issues, bringing together the intellectual strengths of various innovation hubs within the university, as well as high-level academic and policy research experts. Through in-depth exchanges and discussions, it aims to produce a series of high-level think tank achievements characterized by strategic vision, contemporary relevance, and intellectual depth. This platform continuously advances knowledge innovation, theoretical innovation, and policy innovation, while strengthening the positive dynamic of academic disciplines supporting policy consultation and think tanks nurturing academic disciplines.