Clemens Watzenb\"ock, Daniel Aletaha, Micha\"el Deman, Thomas Deimel, Jana Eder, Ivana Janickova, Robert Janiczek, Peter Mandl, Philipp Seeb\"ock, Gabriela Supp, Paul Weiser, Georg Langs

Researchers at the University of Vienna and collaborators have developed ChronoCon, a self-supervised learning method that trains AI models to assess disease severity in medical imaging without expert labels. The approach leverages the chronological order of a patient's longitudinal scans, assuming monotonic progression in irreversible diseases like rheumatoid arthritis, to derive ranking signals for contrastive learning. Tested on radiographs, the method achieves 86% accuracy in severity prediction with fine-tuning on just five patients, substantially outperforming supervised baselines in low-label settings and demonstrating how clinical metadata can reduce annotation burden.