Pediatric cancer recurrence prediction has taken a transformative leap with the advent of innovative AI tools designed to analyze intricate patterns in brain scans over time.A recent Harvard study underscores the enhanced accuracy of these tools compared to traditional methods, especially in predicting relapse risk for children with gliomas.