Comparative analysis of end-to-end learning and foundation models for clinical-grade prostate pathology nature.com

Researchers led by Kimmo Kartasalo and Nita Mulliqi at Karolinska Institutet compared foundation models with task-specific AI on more than 100,000 prostate biopsies from 6,268 patients across 15 sites. Foundation models helped when labeled training data were scarce, but task-specific models matched or exceeded them with more data, reduced errors and technical variability, and used up to 35 times less energy.