Researchers evaluated an AI-assisted PET/CT segmentation approach for large B-cell lymphoma, reporting that automated measurements substantially reduce tumor quantification time without degrading reliability. The retrospective study spanned two institutions and compared segmentation speed and consistency to conventional workflows. In practice, shorter time-to-quantification can reduce delays in staging, response assessment, and trial workflows where volumetric endpoints and time metrics matter. The paper’s focus on reliability addresses a persistent barrier to adoption for automated imaging tools. Further prospective trials are likely needed to confirm generalizability across scanners, protocols, and reading sites—especially as AI segmentation becomes embedded in oncology pipelines.