mlsanityData to 7 Oct 2026

Transcending the Annotation Bottleneck: AI-Powered Discovery in Biology and Medicine

arXiv 2602.20100 ↗First posted 23 Feb 2026cs.CV cs.AI eess.IVcited by 0

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Abstract (arXiv)

The dependence on expert annotation has long constituted the primary rate-limiting step in the application of artificial intelligence to biomedicine. While supervised learning drove the initial wave of clinical algorithms, a paradigm shift towards unsupervised and self-supervised learning (SSL) is currently unlocking the latent potential of biobank-scale datasets. By learning directly from the intrinsic structure of data - whether pixels in a magnetic resonance image (MRI), voxels in a volumetric scan, or tokens in a genomic sequence - these methods facilitate the discovery of novel phenotypes, the linkage of morphology to genetics, and the detection of anomalies without human bias. This article synthesises seminal and recent advances in "learning without labels," highlighting how unsupervised frameworks can derive heritable cardiac traits, predict spatial gene expression in histology, and detect pathologies with performance that rivals or exceeds supervised counterparts.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 23 Feb 2026.

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References · 11

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  11. 2022MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latentcited by 4

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