While targeted radiation therapy is a valuable treatment for brain tumors, one of its potential side effects is radiation necrosis—damage to healthy brain tissue that can appear on MRI scans as a lesion similar to a recurring or progressive tumor. This similarity makes it extremely difficult for radiologists to differentiate between the two using standard MRI imaging alone. A new study led by a professor from York University’s Lassonde School of Engineering has introduced a novel AI-based technique that significantly improves the ability to distinguish between progressive brain tumors and radiation necrosis. The method leverages advanced machine learning algorithms trained on high-resolution, multi-parametric MRI data to detect subtle differences in tissue characteristics that are often imperceptible to the human eye. The results show that the AI model outperforms traditional diagnostic approaches in accuracy and consistency. This advancement could enable earlier and more precise diagnoses, helping clinicians avoid unnecessary surgeries or treatments and improve patient outcomes. The research highlights the growing role of artificial intelligence in enhancing medical imaging analysis, particularly in complex neurological conditions where early and accurate detection is critical.