Pediatric cancer recurrence remains a critical concern in the field of child oncology, particularly for conditions such as gliomas, where the risk of relapse can significantly impact treatment outcomes.Recent advancements in AI in pediatric oncology have shown promise in better predicting the likelihood of such relapses, potentially transforming how pediatric cancer treatment innovations are approached.