Human–artificial intelligence collaboration for comanaging epilepsy in real time

Nature's study on human–artificial intelligence collaboration for comanaging epilepsy in real time demonstrates that a moderated, large language model‑based chat interface integrated with electroencephalography (EEG) monitoring can reduce seizure‑detector false alarms by allowing patients to converse with their monitoring system via a smartphone. For website owners, this proof‑of‑concept signals that LLM‑driven conversational layers will increasingly be embedded in medical‑device dashboards and patient‑portal apps, generating a new class of legitimate, high‑frequency bot traffic that mimics human interaction. Unlike generic crawlers, these bots will exchange structured health data, trigger real‑time alerts, and may persist across sessions, raising both performance and compliance considerations for sites that host health‑related content or APIs. **Actionable tips:** 1. **Tag medical‑AI bots in your llms.txt** with a dedicated `User‑Agent: EpilepsyAI/1.0` line and specify allowed endpoints, so your analytics can separate therapeutic traffic from generic crawlers. 2. **Implement rate‑limiting rules** keyed to the `User‑Agent` and session token to prevent accidental overload during peak seizure‑monitoring periods. 3. **Deploy real‑time bot detection** (e.g., anomaly‑based fingerprinting) that flags deviations from the expected conversational pattern, enabling you to audit and fine‑tune access controls without disrupting patient care.