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Decoding complexity through machine learning is redefining scientific discovery
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DOI：10.1038/s42005-026-02676-7.png)
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Abstract
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As scientific instruments and the literature generate ever larger volumes of data, machine learning (ML) has become essential for organizing, analyzing and interpreting complex information. This Perspective examines how ML accelerates discovery across disciplines, with examples such as brain mapping and exoplanet detection. It also considers situations with different levels of prior knowledge about the underlying phenomenon, outlining strategies to address limitations and exploit ML effectively. Although growing reliance on ML raises challenges for research practice and validation, it is reshaping scientific methods and expanding what can be studied. We also highlight foundation models as a promising route to faster, broader scientific discovery. Machine learning is transforming scientific research. In this perspective, the authors highlight the challenges associated with it, including data quality, algorithmic bias, model explainability, and the fundamental question of whether machine learning makes true discoveries or merely reshuffles existing knowledge.
Keywords:
Machine learning
Scientific discovery
Data analysis
Foundation models
Algorithmic bias
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