Harnessing the application of artificial intelligence in identification of traditional Chinese medicines

The integration of artificial intelligence (AI) into pharmaceutical practice represents a paradigm shift, offering innovative solutions to long-standing challenges in the authentication and quality control of Chinese herbal medicine (CHM).

This study aims to review the current applications and limitations of AI in the image identification, quality control, active ingredient and toxicity assessment, and origin identification of CHM, providing reference and guidance for the future development of AI-assisted traditional Chinese medicine (TCM) research and practice.

A comprehensive literature search was conducted in PubMed, Google Scholar, and CNKI to identify studies on the application of AI in TCM, covering image recognition, quality control, origin identification, phytochemical analysis, and toxicity assessment.

The results show that AI offers significant advantages in the identification of CHM, improving both accuracy and efficiency. In quality control, the combination of AI with spectroscopic and sensory detection enables more objective analyses. When integrated with chromatographic and multi-technique approaches, AI supports the evaluation of complex components and toxicity. In origin identification, models such as support vector machines (SVM) performed well, though their generalizability remains limited by small sample sizes and regional differences. Persistent challenges in data quality, interpretability, and regulatory approval highlight the need for enhanced standardization, rigorous validation, and closer interdisciplinary collaboration.

This review explores the applications and limitations of AI in TCM, covering image identification, quality identification, active ingredient detection, origin identification, and toxicity evaluation, providing new directions for the development of AI in TCM.