In the long effort to make artificial intelligence a trustworthy partner in medicine, a research team has built a brain tumor detection model that does something rare: it not only identifies tumors in MRI scans with 98.6% accuracy, but shows its reasoning in a way a clinician can examine and verify. XAI-BTNet, trained on established benchmark data, weaves explainability into the fabric of its learning rather than appending it as an afterthought — a distinction that may matter as much as the accuracy figures themselves. The deeper question it raises is not whether machines can see what radiolog