A strategy of traditional medicine (TM) is to establish a knowledge infrastructure for knowledge-based policy-making, clinical decision-making，education, and research. We need to increase the availability of e-library, datasets, and knowledge bases through the Internet and other media. Also, Artificial intelligence and TM need to be combined to implement key technologies such as intelligent diagnosis and prescription recommendation.

The application of AI in traditional Chinese medicine (TCM) can be traced back to 1980's when the primary focus is on the development of expert systems, which brought out many research issues such as the automation of TCM diagnosis, TCM knowledge representation and reasoning, and TCM knowledge engineering. Recently, as the AI field made tremendous progress (e.g., breakthrough technologies such as deep learning and successful medical applications), the research of AI in TCM ushered in a new climax. For example, deep learning and image recognition technologies were used for intelligent diagnosis; e-libraries, ontologies, knowledge graphs and other large-scale repositories were constructed for knowledge services; KDD methods were used to gain insights from the TCM big data. AI in TCM will play a major role in the implementation of traditional medicine development strategy, but there is still a lot of R & D work to do. It is important is to attract the attention of experts from both TCM and IT and to strengthen cross-disciplinary collaboration.

Given this, we intend to organize a workshop on Artificial Intelligence in Chinese Medicine, focused on the latest research and the most challenging problems in this field. For example, how to use AI technology such as image recognition to achieve intelligent diagnosis; how to establish large-scale knowledge system for effective knowledge organization; and how to implement intelligent services such as intelligent search, clinical decision support, and health knowledge recommendation.