Can LLMs Decode Epileptic Seizures? The Promise and the Pitfalls

Large Language Models (LLMs) have increasingly found their way into various sectors, with the healthcare industry being no exception. The latest study focusing on epilepsy diagnosis is a prime example of this trend. Researchers tested eight LLMs, including GPT-3.5, GPT-4, and two specialized medical models, on a core diagnostic task: mapping seizure descriptions to seizure onset zones using likelihood estimates.

Performance Close to Clinicians

Remarkably, after some prompt engineering, these models achieved results approaching clinician-level accuracy. To many, this suggests a new frontier for AI in healthcare. But not so fast. The study also found that improvements were strongly influenced by clinician-guided chain-of-thought reasoning. Let's apply some rigor here: if these models are so dependent on expert guidance, can we really claim they're ready for the clinical stage?

the models showed varying performance based on clinical in-context impersonation, narrative length, and language context, which varied by 13.7%, 32.7%, and 14.2%, respectively. These aren't trivial numbers. They highlight that, while LLMs are powerful, their outputs can be erratic when the conditions aren't just right.

The Hallucination Problem

Here’s where things get a bit murky. The models sometimes based their correct predictions on hallucinated knowledge, essentially, fabricated information. This is a stark reminder that interpretability remains a significant hurdle. Color me skeptical, but can we trust a model that can't distinguish between reality and its own inventions?

the issue of inaccurate source citation emerged as a persistent problem. What they're not telling you is that this flaw could lead to serious repercussions if overlooked in a clinical setting. The stakes are as high as they come, involving real human lives.

A Scalable Framework, But Is It Enough?

The researchers behind this study claim that their SemioLLM framework is scalable and adaptable for evaluating LLMs in other clinical disciplines. While this may sound promising, the framework’s reliance on structured benchmarks, rather than chaotic real-world data, raises questions about its true applicability.

What’s the takeaway here? LLMs like SemioLLM offer a tantalizing glimpse into the future of healthcare, but they're not a panacea. Until they can reliably interpret unstructured narratives without hallucinating, their role should be limited to assisting, not replacing, human clinicians.