Landscape and training opportunities in artificial intelligence for biosciences in Latin America: the BiotrAIn project

Abstract

Artificial Intelligence (AI) is redefining biosciences by enabling the analysis of complex, large-scale biological and medical data. Latin America has demonstrated remarkable potential through studies applying AI to biomedical sciences often using locally generated data and addressing regional needs. However, the integration of AI into research and healthcare in this region faces several structural challenges, including insufficient infrastructure, lack of specialized training, fragmented research systems, and continuously constrained funding. To overcome existing limitations, capacity building at the regional level is crucial. This includes developing localized AI training programs and the reinforcement of a collaborative research framework. Collaboration across institutions and countries, along with engagement from industry and government, is key to fostering an enabling environment for the responsible and effective use of AI. One promising response to these challenges is the BiotrAIn project, a partnership involving EMBL-EBI, the University of Costa Rica, and CABANAnet, with support from the Chan Zuckerberg Initiative. The project is structured around three pillars: knowledge exchange, curriculum development, and regional training. BiotrAIn exemplifies how strategically designed, regionally adapted programs can build AI capacity in biosciences and foster more equitable scientific development across Latin America. Strategic efforts like BiotrAIn serve as essential building blocks toward a more robust, ethical, and sustainable AI ecosystem in the region, while offering a model that could be adapted by other regions globally. Ultimately, with coordinated investment, inclusive policies, and collaborative networks, Latin America can position itself as a significant contributor to global AI-driven bioscience.