Reimagining ethnopharmacology with generative AI: Towards inclusive, ethical, and data-driven traditional medicine

Ethnopharmacology explores bioactive compounds rooted in traditional medical knowledge systems and holds immense promise for drug discovery, cultural preservation, and healthcare innovation. However, fragmented documentation, minimal digitization, and limited integration with biomedical frameworks remain major barriers. The advent of generative artificial intelligence (GenAI), including large language models (LLMs) and molecular generation algorithms, offers transformative solutions to these challenges. This narrative review critically examines the application of GenAI in ethnopharmacology and highlights its role in digitizing traditional knowledge, decoding polyherbal formulations, predicting herb-drug interactions, and accelerating phytopharmaceutical discovery. It synthesizes current literature on GenAI tools and methods relevant to ethnopharmacology, considering natural language processing, knowledge graph construction, molecular modeling, and multimodal data integration. A five-phase strategic framework is proposed for the ethical and effective implementation of GenAI. This review narrates real-world applications from Asian (Ayurvedic, Chinese, Japanese, Thai, Vietnamese), African, and Indigenous American medicines systems demonstrate adaptability across cultures. Stakeholder-specific benefits, spanning academia, healthcare, industry, and indigenous communities, are also discussed, along with methodological innovations and ethical considerations. GenAI offers a significant transition in ethnopharmacology by integrating traditional knowledge systems with advanced computational tools to develop inclusive data-driven innovation across global traditional medicine systems.