bioRxivBioinformaticsPreprint
AI4Life Open Calls and Public Challenges: why, how, and what we have learned.
Vera GalinovaiD, Mehdi SeifiiD, Beatriz Serrano‐SolanoiD, Kristína LidayováiD, Damian E. Dalle NogareiD, Agustín Andrés CorbatiD, Joshua TalksiD, Edoardo GiacomelloiD, Estibaliz Gómez‐de‐MariscaliD, Mariana FerreiraiD, Caterina Fuster-BarcelóiD, Juan Manuel BattagliottiiD, Carlos García-López-de-HaroiD, Benjamin SalmoniD, Melisande CroftiD, Si Young YieiD, Guillermo Rey-PaniaguaiD, Xiaotian HuiD, Sungjun ChoiD, Aagam ShethiD, Chhayansh PorwaliD, Xiaomeng LiiD, Ricardo HenriquesiD, Xinyang LiiD, Alexander KrulliD, , J.R. DeschampsiD
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.
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