Imagine walking into a doctor's office, and instead of a human physician, you're greeted by a friendly AI. This AI needs to understand your ailments, just like a human doctor. But how can it effectively gather the necessary information? That's the challenge tackled by researchers in a new study exploring the question-asking abilities of AI in healthcare conversations. The study introduces "HealthQ," a framework for evaluating how well AI, organized into interconnected systems called "LLM chains," can ask relevant questions to get a complete picture of a patient's health. These chains are like specialized teams within the AI's brain, each handling different aspects of the conversation, such as retrieving information or reasoning through complex medical scenarios. The research focused on evaluating how different LLM chains pose questions, examining their relevance, specificity, and ability to extract the full story from simulated patients. Interestingly, they found that more advanced chains, those using techniques like "reflection" and "Chain of Thought," perform significantly better than simpler methods. These techniques allow the AI to review its questions, refine its approach based on patient responses, and even check its own consistency, much like a human doctor might reflect on their diagnostic process. The research suggests that AI doctors can indeed learn to ask effective questions but also highlights the ongoing challenge of ensuring these questions lead to accurate and complete diagnoses. This work opens exciting possibilities for AI in healthcare, where intelligently designed systems could assist doctors, gather crucial information from patients, and ultimately contribute to better healthcare decisions. However, it also emphasizes the need for robust evaluation frameworks to ensure these powerful AI tools are used safely and responsibly, paving the way for AI-driven improvements in diagnosis and patient care.