AI seizure detector achieves 99% accuracy on real EEG data — but only in lab conditions

Researchers built a neural network that identifies epileptic seizures from brain electrical signals with 99% accuracy by analyzing different frequency bands separately. The system works well in controlled research settings, but the critical question is whether it performs as well on messy real-world EEG recordings from actual patients in hospitals, where signal quality, electrode placement, and individual variation are unpredictable.

This represents incremental progress in seizure detection accuracy, but the paper doesn't show whether the system works on patients it hasn't seen before, or on EEG data from different equipment and hospital settings — the actual prerequisites for deployment. The dramatic accuracy numbers (99.01% overall) are measured on a single dataset (CHB-MIT), which is a controlled research benchmark, not a real clinical rollout. Real signal here would be: deployment in a hospital system, accuracy measured on new patients, comparison to what neurologists actually achieve, and whether false positives (wrong alarms) cause enough clinical harm to matter.