Transcending the Annotation Bottleneck: AI-Powered Discovery in Biology and Medicine
This paradigm shift reduces reliance on costly annotations, accelerating AI applications in biology and medicine.
The paper addresses the bottleneck of expert annotation in AI for biomedicine by shifting from supervised to unsupervised and self-supervised learning, enabling discovery of novel phenotypes and achieving performance that rivals or exceeds supervised methods.
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.