Traditional medicine (TM) represents a vast body of knowledge and practices aimed at preventing, diagnosing, improving, or treating both physical and mental illnesses. Despite significant advancements, there remain critical challenges that demand ongoing research in this field. These include issues such as the variability and the poorly defined composition of traditional medicine preparations, the lack of standardization, and insufficient quality data on their efficacy. Moreover, there are ongoing debates about how best to integrate traditional and biomedical practices within modern healthcare systems. The advent of artificial intelligence (AI) has introduced a new dimension to this domain. Recent breakthroughs in AI, including machine learning and deep learning, have begun to enhance the efficacy, understanding, and procedural efficiency of traditional medicine.
This Research Topic aims to explore the transformative potential of AI in traditional medicine, while addressing the significant hurdles that accompany its application. The focus is to investigate how AI can optimize the discovery and development process in traditional medicine, improve efficacy predictions, and efficiently integrate traditional healing approaches into contemporary healthcare practices.
To gather further insights into this area, we welcome articles addressing, but not limited to, the following themes:
AI Utilization in Traditional Medicine: Applications of deep learning and machine learning for data analysis and pharmacodynamics.
AI and Network Analysis in Traditional Medicine: Exploring herbal network relationships and complex mechanisms through AI technology.
AI-driven Redevelopment of Traditional Medicines: Utilizing AI for novel applications and target discovery in traditional medicine.
AI in Traditional Medicine Target Prediction: Utilizing AI to predict interactions and potential therapeutic targets.
Novel Combinations of Traditional Medicines: Using AI to predict and analyze effective combinations for disease treatment.
Prospective Studies and Reviews: Forward-looking research and comprehensive reviews on AI applications in traditional medicine.
Important Note:
All contributions to this Research Topic must follow the guideline listed in this section:
• The introduction needs to describe the background of the research object focusing on the traditional or local use of a traditional medicine and provide bibliographical references that illustrate its recent application in general healthcare.
• Network studies must critically assess the pharmacological evidence to evaluate the potential effects of a preparation / herbal (medical) product and the limitations of the evidence.
• Purely in silico/AI-based studies are outside of our scope.
• In general, network analysis must be conducted in combination with experimental pharmacology (in vitro or in vivo) or are based on a sound body of experimental pharmacology.
• Chemical anti-oxidant assays like the DPPH or ABTS assay are of no pharmacological relevance, Therefore they can only be used a chemical-analytical assays without pharmacological claims.
• Please self-assess your MS using the ConPhyMP tool (https://ga-online.org/best-practice) and submit the relevant sections of the tool with your submission. You need to follow the standards established in the ConPhyMP statement Front. Pharmacol. 13:953205).
• All the manuscripts need to fully comply with the
Four Pillars of Best Practice in Ethnopharmacology (you can freely download the full version
here). Importantly, please ascertain that the ethnopharmacological context is clearly described (pillar 3d) and that the material investigated is characterized in detail (
pillars 2 a and b).
The topic editor Jidong Lang was employed by the Tasly Pharmaceutical Group Co., Ltd. The remaining editors declare that the proposal was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.