Researchers presented a retrospective case-control study showing that an AI pipeline can automatically quantify ablative margins on intraprocedural CT and predict local tumor progression after radiofrequency ablation for hepatocellular carcinoma. The approach focuses on automated measurement during the procedure window, where margins and geometry can change rapidly and where recurrence risk is often determined. If validated prospectively, the technique could enable faster feedback loops for clinicians deciding whether to extend ablation zones. The study’s design suggests an operational pathway to integrate AI-based imaging quantification into interventional oncology workflows rather than relying solely on retrospective planning contours.