Acupuncture is widely used around the whole world nowadays and exhibits significant efficacy against many chronic diseases, especially in pain-related diseases. With the rapid development of artificial intelligence (AI), its implementation into acupuncture has achieved a series of significant breakthroughs in many areas of acupuncture practice, such as acupoints selection and prescription, acupuncture manipulation identification, acupuncture efficacy prediction, and so on. The paper will discuss the significant theoretical and technical achievements in AI-directed acupuncture. AI-based data mining methods uncovered crucial acupoint combinations for treating various diseases, which provide a scientific basis for acupoints prescription in clinical practice. Furthermore, the rapid development of modern TCM instruments facilitates the integration of modern medical instruments, AI techniques, and acupuncture. This integration significantly improves the quantification, objectification, and standardization of acupuncture as well as the delivery of clinical personalized acupuncture therapy. Machine learning-based clinical efficacy prediction of acupuncture can help doctors screen patients who may benefit from acupuncture treatment. However, the existing challenges require additional work for developing AI-directed acupuncture. Some include a better understanding of ancient Chinese philosophy for AI researchers, TCM acupuncture theory-based explanation of the knowledge discoveries, construction of acupuncture databases, and clinical trials for novel knowledge validation. This review aims to summarize the major contribution of AI techniques to the discovery of novel acupuncture knowledge, the improvement for acupuncture safety and efficacy, the development and inheritance of acupuncture, and the major challenges for the further development of AI-directed acupuncture. The development of acupuncture can progress with the help of AI.

To investigate the main chemical compositions of Verbena officinals L. extract, a qualitative high performance liquid chromatography-photodiode array-high resolution mass spectrometry (HPLC-PDA-HRMS) method was established. The structures of the compounds detected were identified by analyzing the chromatographic profiles and the corresponding mass spectra obtained by full scan and MSn full scan. Twenty one compounds including iridoid glycosides, flavonoids, triterpenoids, phenylpropanoids and phenolic diterpenoids were identified, and six of them were not reported in other literatures about Verbena officinalis L. extract, such as carnosic acid, carnosol, rosmanol, isorosmanol, rosmarinic acid and acacetin-7-O-rutinoside. This method is simple, rapid, accurate and sensitive. It provides a reliable scientific basis for the identification of the authenticity and quality control of Chinese medicinal materials.