AI for herbal medicine

The “AI for Traditional Chinese Herbal Medicine” project is an innovative intersection of artificial intelligence (AI) and traditional Chinese medicine (TCM), aiming to revolutionize the way herbal remedies are studied, developed, and utilized. This project’s motivation stems from the complex nature of TCM, where the efficacy of herbal treatments depends on a multitude of factors, including plant species, harvest time, and preparation methods. AI, with its robust data processing and pattern recognition capabilities, offers a powerful tool to unravel these complexities.

The core of this project involves using machine learning algorithms and data analytics to analyze vast amounts of historical and contemporary data on herbal medicines. This includes patient outcomes, herbal compositions, and their pharmacological effects. By doing so, the project seeks to identify effective herbal combinations, predict therapeutic effects, and personalize treatments for individual patients.

Significantly, this project also focuses on digitizing and translating ancient TCM texts and pharmacopoeias, converting centuries-old knowledge into accessible data for AI analysis. This fusion of traditional knowledge and modern technology not only preserves ancient wisdom but also enhances it with new insights and applications.

The work completed so far in this project has shown promising results in areas like identifying potential herbal treatments for modern diseases, optimizing herbal formulations, and even aiding in new drug discovery. The next steps involve further refining the AI models, expanding the herbal database, and integrating these findings into clinical practice to provide more effective and personalized healthcare solutions..

My recent publications in this field:

1. Y. Yao et al., “An ontology-based artificial intelligence model for medicine side-effect prediction: taking traditional chinese medicine as an example,” Computational and Mathematical Methods in Medicine, vol. 2019, pp. 1–7, Oct. 2019, doi: 10.1155/2019/8617503.

2. Z. Wang, L. Li, J. Yan, and Y. Yao, “Approaching high-accuracy side effect prediction of traditional chinese medicine compound prescription using network embedding and deep learning,” IEEE Access, vol. 8, pp. 82493–82499, 2020, doi: 10.1109/ACCESS.2020.2991750.

3. Z. Wang, L. Li, M. Song, J. Yan, J. Shi, and Y. Yao, “Evaluating the traditional chinese medicine (TCM) officially recommended in china for COVID-19 using ontology-based side-effect prediction framework (OSPF) and deep learning,” Journal of Ethnopharmacology, vol. 272, p. 113957, Feb. 2021, doi: 10.1016/j.jep.2021.113957.