Researchers published findings in Scientific Reports showing a deep learning model that rapidly analyses procurement kidney biopsies to predict transplant outcomes and assist pathology assessments. The team demonstrated that algorithmic image analysis can standardize and accelerate biopsy reads that currently vary across centers and pathologists. The paper details model training on digitized biopsy specimens and validation against recipient outcomes, suggesting AI could triage marginal organs and inform acceptance decisions. For transplant surgeons, pathologists, and organ allocation bodies, implementation could change organ utilization workflows by providing reproducible pathology-derived risk scores in near real time.