Winners Ammodo Science Award 2024 announced

The winners of the Ammodo Science Award for groundbreaking research 2024 are the Computational Pathology Group (Radboud University), Past ❤ Play (Leiden University), the Lipidomics Team (University of Groningen), and Psychological Methods Lab (University of Amsterdam). Each winner has been granted substantial cash prizes to support further research, with EUR 1.6 million allocated to Natural Sciences and Biomedical Sciences, and EUR 800,000 to Humanities and Social Sciences.

"We are extremely happy and honored to be selected, as it is a great recognition of the work we have done over the years. Especially the fact that the ‘team science’ that we have been practicing for many years is one of the major criteria makes the award very special", says Jeroen van der Laak.

Read the full article on the winners of the Ammodo Science Award.

The Computational Pathology Group's dedication to turning computers into virtual pathologists has not only been recognized but also generously funded to propel their research forward. With a grant of EUR 1.6 million, the team's ambitious project ANTONI is set to become one of the world's first AI models capable of making independent clinical diagnoses across various cancers. What sets ANTONI apart is its ability to provide 'explainable AI', a breakthrough feature that allows clinicians to understand the rationale behind its diagnoses, enhancing reliability and efficiency in patient care. This accolade underscores the Computational Pathology Group's role at the forefront of integrating AI into healthcare, heralding a new era of innovation in pathology.

On the question how the award grant will be used, Jeroen says: "As part of the nomination, we have written a research proposal, in which we want to develop a so-called ‘foundation model’ for pathology. This is the next generation of AI models, which will help us to create even more powerful AI. Not only are the AI solutions more powerful, they can also be developed with less data and we can add a layer of explainability: the model will be capable of giving insight in the ‘reasoning’ behind it’s outputs."

Learn more about the Computational Pathology Group.