ABSTRACT:
West Africa holds one of the world's richest bodies of ethnomedicinal plant knowledge which has accumulated over centuries through the practices of Yoruba, Igbo, Hausa, Ewe, Akan, and other indigenous medical traditions. This knowledge is however eroding and traditional healers are ageing. The documentation of these medicinal practices remains fragmented and the plants themselves face mounting pressure from habitat loss and unsustainable harvesting. Artificial intelligence now offers a practical set of tools such as computer vision for accurate field identification of medicinally important species, natural language processing for mining and structuring ethnobotanical records, machine learning for predicting bioactive properties of documented plant compounds, and citizen-science platforms for scalable community-based data collection. These tools have been able to address the different stages of challenges being faced by indigenous medical traditions. Several species central to West African ethnomedicine, including Vernonia amygdalina, Azadirachta indica, Carica papaya and Mangifera indica are used to illustrate the pipeline from field documentation through AI-assisted identification to computational phytochemical screening. This paper thus reviews the current state of these applications, grounds them in the specific ethnobotanical landscape of West Africa, and argues for an integrated approach that connects AI-assisted documentation with pharmacological validation and genuine community partnership. The paper also confronts uncomfortable realities of these tools such as dataset biases, digital divides and the risk that AI-assisted bioprospecting extracts value from communities without reciprocating it.