MedFuzz: Exploring the robustness of LLMs on medical challenge problems
Large language models (LLMs) have achieved unprecedented accuracy on medical question-answering benchmarks, showcasing their potential to revolutionize healthcare by supporting clinicians and patients. However, these benchmarks often fail to capture the full complexity of real-world medical scenarios. To truly harness the power of LLMs in healthcare, we must go beyond these benchmarks by introducing challenges that bring us closer to the nuanced realities of clinical practice.
Introducing MedFuzz
Benchmarks like MedQA rely on simplifying assumptions to gauge accuracy. These assumptions distill complex problems that highlight