Abstract
Artificial intelligence (AI) presents a transformative opportunity to significantly enhance the safety and efficacy of herbal medicines derived from medicinal plants and natural products. Current methods for phytochemical profiling and toxicity assessment are often time-consuming, expensive, and limited in scope, hindering the efficient development and safe use of these increasingly popular remedies. AI-powered tools, leveraging deep learning and machine learning algorithms, offer a powerful solution. These tools can automate the identification and quantification of phytochemicals, dramatically improving the speed and accuracy of analysis. Furthermore, AI's predictive capabilities allow for the assessment of individual compound toxicity and the complex interactions between multiple phytochemicals, enabling earlier detection of potential safety concerns and guiding the development of safer formulations. Beyond individual compound analysis, AI can integrate diverse datasets, including genomic, metabolomic, and ethnobotanical information, to accelerate the discovery and development of novel therapeutics from natural sources. However, realizing the full potential of AI requires high-quality datasets, robust interdisciplinary collaboration, and careful attention to issues of data bias and model interpretability. Investing in AI research within the field of phytochemistry is crucial for realizing safer and more effective herbal medicines, ultimately contributing to improved global health outcomes.