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

Galinova, V.; Seifi, M.; Serrano Solano, B.; Lidayova, K.; Dalle Nogare, D.; Corbat, A. A.; Talks, J.; Giacomello, E.; Gomez-de-Mariscal, E.; Ferreira, M. G.; Fuster-Barcelo, C.; Battagliotti, J. M.; Garcia-Lopez-de-Haro, C.; Salmon, B.; Croft, M.; Yie, S. Y.; Rey-Paniagua, G.; Hu, X.; Cho, S.; Sheth, A.; Porwal, C.; Li, X.; AI4Life Consortium, ; Henriques, R.; Li, X.; Krull, A.; Klemm, A.; Munoz Barrutia, A.; Kreshuk, A.; Ouyang, W.; Jug, F.; Deschamps, J.

2026-07-22 bioinformatics
10.64898/2026.07.21.739486 bioRxiv
Show 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.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

1
Nature Methods
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Top 0.1%
53.1%
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50% of probability mass above
2
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6.6%
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6
Nature
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Top 6%
2.1%
7
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Top 2%
1.9%
8
PLOS Computational Biology
● 1863 papers in training set
Top 13%
1.9%
9
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Top 17%
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10
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Top 6%
1.4%
11
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12
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Top 64%
1.1%
13
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1.1%
14
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Top 2%
1.0%
15
Cell Reports Methods
○ 165 papers in training set
Top 3%
1.0%
16
GigaScience
◐ 212 papers in training set
Top 4%
0.8%
17
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◐ 242 papers in training set
Top 5%
0.6%
18
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Top 4%
0.6%
19
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Top 71%
0.6%
20
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Top 5%
0.6%
21
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Top 9%
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.