Imagine asking an AI chatbot for medical advice. Sounds futuristic, right? But what if the AI hallucinates, giving you inaccurate or even harmful information? This is a critical challenge researchers are tackling, especially in medical AI where misinformation can have serious consequences. A new research paper dives deep into this problem, exploring how to measure uncertainty in Large Language Models (LLMs) used for medical question answering. The core issue? Current LLMs can sometimes give wrong answers with surprising confidence, making it hard to know when to trust their advice. The researchers tested various methods to gauge how sure an LLM is about its medical answers. Existing approaches, like measuring the 'entropy' of the AI's responses, often fall short. High entropy suggests the model is unsure, while low entropy implies confidence. But in complex medical scenarios, an AI can be confidently incorrect, producing a misleadingly low entropy. So, what's the solution? The researchers propose a clever 'Two-phase Verification' method. First, the LLM generates an answer along with a step-by-step explanation. Then, it creates verification questions targeting each step of its explanation. The LLM answers these questions twice—once independently, and once using its original explanation as a reference. Any inconsistencies between the two sets of answers indicate uncertainty. This innovative method proves more reliable than existing approaches, providing a more robust way to identify potential hallucinations. Think of it like double-checking your work. The results are promising. The Two-phase Verification method was tested using Llama 2 Chat models on three medical question-answering datasets. It consistently outperformed other uncertainty estimation techniques, showing greater stability and scalability across different models and datasets. The research highlights the crucial need for reliable uncertainty estimation in medical AI. As AI-powered medical chatbots and diagnostic tools become more prevalent, knowing when to trust their output is paramount. This research is a significant step toward ensuring that AI provides safe and reliable medical guidance, paving the way for more responsible integration of AI in healthcare. While this research demonstrates a significant leap forward, challenges remain. Improving how verification questions are generated and integrating more specialized medical knowledge into LLMs are key areas for future research. The ultimate goal? To build AI systems that not only provide accurate medical information but also know when to say, 'I'm not sure, let’s consult a human expert.'