[Report] Research on the Governance System of Trustworthy Multimodal Scientific Big Data in Healthcare

Sunday, Sep 28, 14:30 p.m

Editor:College of Information Science and Technology Time:2025-09-12

Speaker: Dr Fang Yang

Time: Sunday, Sep 28, 14:30 p.m

Venue: Academic Lecture Hall (B1 Floor), High-End Technology Building, East Campus, BUCT

Abstract:

Faced with massive healthcare data resources, we start from the industry pain points and propose an integrated pathway of "Data → Scientific Data → Evidence → Application → Industry". We establish three standards including RCDM, SCDM and GCDM as well as a full-stack toolkit featuring "One Brain, Multiple Terminals", and build a federated collaboration network that enables "data to stay within domains while models can be interconnected", so as to accelerate the generation of multi-center evidence. Additionally, we construct AI-ready data assets and an intelligent platform for research and translation (Agentic Cohort, AigenMed), providing replicable, quantifiable and regulable high-quality data and evidence-based capabilities for governments, hospitals, research institutions and industries. We will continue to evolve and empower stakeholders, driving the collaborative leap-forward development of healthcare innovation and industrialization.

Biography:

Yang Fan is a Professor and Doctoral Supervisor at Shandong University, concurrently serving as a Young Expert of the Taishan Scholars Program in Shandong Province, Vice Dean of the National Institute of Health Medical Big Data, Executive Deputy Director of the Shandong Provincial Engineering Research Center for Digital and Intelligent Proactive Health, a peer review expert for the HJ Talent Program of the Ministry of Science and Technology, a post review expert for biomedical industry talents of the Ministry of Industry and Information Technology, and a Senior Member of the China Computer Federation. He has been awarded the Written Commendation for "COVID-19 Scientific and Technological Tackling" by the Ministry of Science and Technology and the Second-Class Engineering Technology Award by the Ministry of Education. His main research interests lie in causal inference-driven trustworthy deep learning and tumor informatics. He has presided over 10 projects including topics under the National Key R&D Program, published more than 30 SCI/EI-indexed papers, obtained 25 authorized or pending national invention patents, accomplished 2 technology transfer cases, secured 12 software copyrights, and formulated 2 industry and association standards.