[Application scenario design and prospect of generative artificial intelligence (AI) in intelligent manufacturing and supply chain of traditional Chinese medicine].

Journal: Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica
PMID:

Abstract

Intelligent manufacturing technologies, including databases, mathematical modeling, and information systems have played a significant role in process control, production management, and supply chain management in traditional Chinese medicine(TCM) industry. However, their ability to process and utilize unstructured data, such as research and development reports, batch production records, quality inspection records, and supplier documents, is relatively weak. For text, images, language, and other unstructured data, generative artificial intelligence(AI) technology has shown strong potential for development in extracting information, extracting knowledge, semantic retrieval, and content generation. Generative AI is expected to provide a feasible set of tools for the utilization of unstructured data resources in the TCM industry. Based on years of research and industrial application experience in TCM intelligent manufacturing technology, this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data, analyzed the application value of generative AI in the TCM manufacturing process and supply chain, summarized four typical application scenarios, including intelligent pharmaceutical knowledge base/knowledge graph, intelligent on-the-job trai-ning, intelligent production quality control, and intelligent supply chain. Furthermore, this study also explained the data collection and processing, business process design, application potential, and value of each scenario based on industry demands. Finally, based on the integration of generative AI and TCM industrial models, the study proposed a preliminary concept of a smart industrial brain for TCM, aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.

Authors

  • Hao-Shu Xiong
    National-Local Joint Engineering Laboratory of Advanced Traditional Chinese Medicine Manufacturing Technology,Tasly Pharmaceutical Group Co., Ltd. Tianjin 300410, China National Key Laboratory of Chinese Medicine Modernization Tianjin 300410, China.
  • Bei-Xuan Wang
    National-Local Joint Engineering Laboratory of Advanced Traditional Chinese Medicine Manufacturing Technology,Tasly Pharmaceutical Group Co., Ltd. Tianjin 300410, China National Key Laboratory of Chinese Medicine Modernization Tianjin 300410, China.
  • Jian Hou
    Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China.
  • Cheng Zhao
    Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Ya-Wen Wang
    School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Shun-Nan Zhang
    National-Local Joint Engineering Laboratory of Advanced Traditional Chinese Medicine Manufacturing Technology,Tasly Pharmaceutical Group Co., Ltd. Tianjin 300410, China National Key Laboratory of Chinese Medicine Modernization Tianjin 300410, China.
  • Kai-Jing Yan
    National-Local Joint Engineering Laboratory of Advanced Traditional Chinese Medicine Manufacturing Technology,Tasly Pharmaceutical Group Co., Ltd. Tianjin 300410, China National Key Laboratory of Chinese Medicine Modernization Tianjin 300410, China.