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Using AI in bioimage analysis to elevate the rate of scientific discovery as a community

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The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI). For this trend to continue in a way most useful for stimulating scientific progress, it will require our multidisciplinary community to work together, establish FAIR (findable, accessible, interoperable and reusable) data sharing and deliver usable and reproducible analytical tools.

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Fig. 1: To achieve the overarching goal of elevating the rate of scientific discovery in the life sciences, all members of our community must work together in mutually beneficial ways.

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Acknowledgements

M.H. and F.J. received funding by the European Commission through the Horizon Europe program (AI4LIFE project, grant agreement 101057970-AI4LIFE).

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Authors and Affiliations

  1. Fondazione Human Technopole, Milan, Italy

    Damian Dalle Nogare, Joran Deschamps & Florian Jug

  2. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK

    Matthew Hartley

  3. Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    Jan Ellenberg

Authors
  1. Damian Dalle Nogare
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  2. Matthew Hartley
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  3. Joran Deschamps
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  4. Jan Ellenberg
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  5. Florian Jug
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Corresponding author

Correspondence to Florian Jug.

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The authors declare no competing interests.

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Nogare, D.D., Hartley, M., Deschamps, J. et al. Using AI in bioimage analysis to elevate the rate of scientific discovery as a community. Nat Methods 20, 973–975 (2023). https://doi.org/10.1038/s41592-023-01929-5

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