Qingsong Health's QSevidence partners with China Academy of Chinese Medical Sciences to build an intelligent research support platform

Recently, the Artemisinin Research Center of the China Academy of Chinese Medical Sciences and Qingsong Health's "QSevidence" launched a collaboration to build a traditional Chinese medicine (TCM) intelligent platform. Based on their respective foundations in TCM scientific research and medical artificial intelligence, the two parties will engage in exploratory collaboration in intelligent TCM data processing, research knowledge organization, research support tools, and technology transfer, jointly advancing the practical application of artificial intelligence technology in TCM scientific research scenarios.

TCM scientific research involves multiple types of knowledge and data, including ancient classics, modern literature, pharmaceutical information, experimental data, and research findings, characterized by massive volume, dispersed sources, high specialization, and complex interrelationships. As research materials continue to grow, researchers need to invest a significant amount of time in literature retrieval, information screening, knowledge structuring, and evidence analysis. How to leverage artificial intelligence to improve the organization and utilization efficiency of research knowledge has become an important topic in the digital and intelligent development of TCM scientific research.

In terms of TCM data intelligence, the two parties will explore standardized governance, correlation analysis, and intelligent utilization of multi-source and multi-type research data, promoting the transformation of dispersed data into structured research resources to provide a more solid data foundation for research design, evidence analysis, and knowledge discovery.

In terms of research knowledge organization, the two parties will explore the application of knowledge engineering and artificial intelligence technology to systematically organize TCM classics, modern research literature, pharmaceutical information, and research findings, gradually forming a connectable, traceable, and continuously updated professional knowledge system to enhance the efficiency of TCM knowledge retrieval, comprehension, analysis, and comprehensive utilization.

In terms of intelligent research support, QSevidence will focus on the actual work needs of researchers, exploring the application of artificial intelligence across links including literature retrieval and review, research material summarization, evidence organizing, research clue discovery, project design assistance, and research outcome management. By reducing repetitive information processing tasks, it will help researchers locate relevant materials, outline research threads, and discover potential clues more quickly, allowing them to devote more energy to professional judgment, scientific verification, and original research.

Addressing the multi-disciplinary and multi-institutional collaborative nature of TCM research, the two parties will also explore using intelligent tools to boost research knowledge sharing and project collaboration efficiency. This will facilitate effective connections among research forces in Chinese medicine, Chinese materia medica, pharmacology, chemistry, bioinformatics, data science, and artificial intelligence, providing more efficient synergistic support for interdisciplinary research.

In terms of technology transfer, the two parties will focus on connecting the application of AI research tools, professional knowledge services, and innovative research findings. They will explore establishing a collaborative mechanism spanning knowledge accumulation, research discovery, outcome verification, and scenario application, enabling research knowledge and technical achievements to serve TCM research and related practices more efficiently.

TCM knowledge possesses distinct characteristics of specialization, systematicness, and empiricism. The application of artificial intelligence in related research scenarios requires not only technical innovation, but also authoritative expert participation, professional knowledge validation, and rigorous research standards. The platform construction will adhere to the principle of "expert-led, AI-assisted," emphasizing knowledge source traceability, professional content review, data security, and research ethics, while exploring the establishment of application mechanisms where results are traceable, experts can review, and effectiveness can be evaluated.

The construction of this intelligent research support platform marks a deeper penetration of artificial intelligence technology into core TCM research scenarios. It also signifies that Qingsong Health's QSevidence artificial intelligence capabilities are extending further from business applications for health services to the source of medical knowledge production and scientific research innovation.

In the future, the two parties will continue to advance technology R&D, research tool construction, application validation, academic collaboration, and technology transfer. They will explore optimizing TCM research workflows with artificial intelligence, boosting knowledge discovery and collaborative research efficiency, and fostering deep connections among traditional TCM knowledge, modern scientific methods, and digital technologies, thereby providing a new technological path for the modernization, digitization, and internationalization of traditional Chinese medicine.