Imagine a world where AI could decipher the complex language of seizures, offering crucial insights into epilepsy diagnosis. That's the ambitious goal of SemioLLM, a groundbreaking research project exploring how Large Language Models (LLMs) can analyze patient seizure descriptions to pinpoint the source of these neurological events. Epilepsy, a disorder affecting millions, is characterized by unpredictable seizures originating from specific brain regions. Identifying these regions is critical for effective treatment, particularly for drug-resistant cases where surgery is an option. SemioLLM delves into whether AI can interpret the subtle clues within patient narratives, aiding clinicians in this challenging diagnostic process. Researchers tested cutting-edge LLMs like GPT-4 and Mixtral on a vast database linking seizure descriptions to brain regions. The results? Promising, but not without hurdles. By carefully crafting prompts, the AI models demonstrated an ability to pinpoint the seizure origin with accuracy exceeding random chance, even rivaling experienced clinicians in some instances. This suggests LLMs possess a latent capacity to decipher the complex semiology of seizures, potentially revolutionizing epilepsy diagnosis. However, challenges remain. Some models exhibited overconfidence despite inaccurate predictions, and others struggled with citing medical literature correctly, sometimes even hallucinating sources. This underscores the need for rigorous testing and refinement before deploying such AI tools in real-world clinical settings. Ensuring reliability and trustworthiness is paramount. While SemioLLM offers a glimpse into the exciting potential of AI in epilepsy care, it also highlights the importance of careful evaluation. As research continues, we can expect further improvements in accuracy, reliability, and interpretability of LLM-based diagnostic tools, bringing us closer to a future where AI can truly assist clinicians in decoding the complexities of epilepsy.