AI4Life Open Calls and Public Challenges: why, how, and what we have learned

Authors: Vera Galinova, Mehdi Seifi, Beatriz Serrano Solano, Kristína Lidayová, Damian Dalle Nogare, Agustín Andrés Corbat, Joshua Talks, Edoardo Giacomello, Estibaliz Gómez-de-Mariscal, Mariana G. Ferreira, Caterina Fuster-Barceló, Juan Manuel Battagliotti, Carlos García-López-de-Haro, Benjamin Salmon, Melisande Croft, Si Young Yie, Guillermo Rey-Paniagua, Xiaotian Hu, Sungjun Cho, Aagam Sheth, Chhayansh Porwal, Xiaomeng Li, AI4Life Consortium, Ricardo Henriques, Xinyang Li, Alexander Krull, Anna Klemm, Arrate Muñoz Barrutia, Anna Kreshuk, Wei Ouyang, Florian Jug, Joran Deschamps
Preprint published in bioRxiv, July 2026
Publisher: Cold Spring Harbor Laboratory

DOI: 10.64898/2026.07.21.739486

ABSTRACT: Within AI4Life, we ran three Open Calls and three Public Challenges (2023-2025), supporting 22 bioimage analysis projects from 151 applications and engaging 225 challenge participants, with the aim of applying FAIR deep learning in the life sciences. Our experience offers a view of the current state of bioimage analysis, the landscape of available tools, as well as the existing gaps between method developers, tool producers and potential users. It highlights that even after careful selection for AI-ready projects, most still require substantial effort to apply deep learning, and that the field still relies heavily on established, well-rounded methods to solve common problems. We come to the conclusion that for scientific AI in biology, the rate-limiting step is not methods and models but data, annotations, and shared infrastructure underneath them.