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

@article{molinamora2026landscape,
  title = {{Landscape and training opportunities in artificial intelligence for biosciences in Latin America: the BiotrAIn project.}},
  author = {Jose Arturo Molina-Mora and Flavio Ezequiel Spetale and Tulio de Lima Campos and Pedro E Romero and Lucas Inchausti and Adrian Gustavo Turjanski and Ignacio Alcántara and Maria Fernanda Ribeiro Dias and Carla V Filippi and Matías Domínguez and Juan Bizzotto and Daniela Senra and J Noé García-Chávez and Claudia Hoepfner and Cleidy Osorio-Mogollon and C Paola Rangel-Chávez and Valeria Foronda and Alejandra Rougon-Cardoso and Nelly Selem-Mojica and Yesid Cuesta-Astroz and Cindy Aguilar-Bartels and Rebeca Campos-Sánchez and Lizzie Tchongwe Divala and Kim Tamara Gurwitz and Cath Brooksbank},
  journal = {Frontiers in artificial intelligence},
  year = {2026},
  month = oct,
  doi = {10.3389/frai.2026.1931300},
  url = {https://europepmc.org/article/MED/42952854},
  note = {CC BY},
}

TY  - JOUR
TI  - Landscape and training opportunities in artificial intelligence for biosciences in Latin America: the BiotrAIn project.
AU  - Jose Arturo Molina-Mora
AU  - Flavio Ezequiel Spetale
AU  - Tulio de Lima Campos
AU  - Pedro E Romero
AU  - Lucas Inchausti
AU  - Adrian Gustavo Turjanski
AU  - Ignacio Alcántara
AU  - Maria Fernanda Ribeiro Dias
AU  - Carla V Filippi
AU  - Matías Domínguez
AU  - Juan Bizzotto
AU  - Daniela Senra
AU  - J Noé García-Chávez
AU  - Claudia Hoepfner
AU  - Cleidy Osorio-Mogollon
AU  - C Paola Rangel-Chávez
AU  - Valeria Foronda
AU  - Alejandra Rougon-Cardoso
AU  - Nelly Selem-Mojica
AU  - Yesid Cuesta-Astroz
AU  - Cindy Aguilar-Bartels
AU  - Rebeca Campos-Sánchez
AU  - Lizzie Tchongwe Divala
AU  - Kim Tamara Gurwitz
AU  - Cath Brooksbank
PY  - 2026
DA  - 2026/10/14
JO  - Frontiers in artificial intelligence
DO  - 10.3389/frai.2026.1931300
UR  - https://europepmc.org/article/MED/42952854
AB  - 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.
SN  - 2624-8212
ER  -