Two developments sharpen the evidence base around cryoablation for cancer. One new AI model aims to predict cryoablation failure by measuring tumor contact with the renal sinus, using imaging to identify patients most likely to have viable tissue after treatment. In parallel, an earlier study reports that AI-assisted tumor contact quantification can improve procedural decision-making for small kidney tumors, addressing a core limitation of image-guided cryoablation: not every frozen lesion stays frozen. Together, these efforts signal a practical shift toward real-time or near-real-time analytics during ablation workflows. For clinicians, the goal is to reduce repeat procedures and improve local control by anticipating when inadequate tumor freezing is most likely. The cluster also highlights how imaging-derived features are increasingly being operationalized into algorithmic risk stratification tools rather than purely retrospective risk scoring.