When medications fail to control epilepsy, doctors may suggest surgery: removing the brain region where seizures originate. But predicting whether it will help a particular person is tricky. Pre-surgery MRI scans show only the current state, not how the brain will change after the intervention.

The new approach works like a librarian searching for similar medical histories. The program takes two scans, before and after surgery, and compresses the difference between them into a short code of 512 numbers. It then looks through an archive for cases with a similar code: people whose brains changed in the same way. Based on these examples, the program makes a prediction: did the surgery help, and why.

The accuracy of predictions was rated on a scale from 0 to 1 (the closer to 1, the better). The program scored between 0.834 and 0.905, while the old method, which looks at only one scan, scored 0.793. That's a noticeable step forward, but so far the system has been trained on 268 pairs of scans from an open database. The authors honestly admit: the program might have learned not the improvement itself but simply the location of the surgery — for example, the temple or another area. So this is more of a proof of concept than a ready-to-use hospital tool.

The first step is done: the program can find similar cases and explain its prediction without hallucinations — fabricated facts. If more data from different clinics are added, accuracy will improve, and then the decision about surgery will become more informed.