Imagine an AI doctor that can answer complex medical questions, pulling from a vast database of medical knowledge. Sounds like science fiction? Large Language Models (LLMs) are stepping into this very role, demonstrating impressive abilities to process and apply information. But how do these AI doctors stack up against real human physicians? Researchers put this to the test in a fascinating study where they quizzed LLMs like Chat-GPT4 and Claude3-Opus with over 24,000 medical questions, covering everything from symptoms and diagnoses to lab tests and medical statistics. These weren't simple yes/no questions, but multi-choice queries designed to mimic the real-world diagnostic challenges faced by doctors. What they found was intriguing. LLMs excelled at questions requiring understanding of medical concepts, like differentiating diseases. However, they stumbled when asked to interpret numerical data – the very kind of data crucial for making evidence-based decisions, such as evaluating the likelihood of a particular diagnosis based on symptoms and test results. Interestingly, Claude3 showed a slight edge over GPT4 in handling numerical questions. But perhaps the most telling result came from the human control group: experienced doctors consistently outperformed both LLMs, especially on questions requiring numerical reasoning. So, while AI has made incredible strides in medicine, it seems human doctors aren’t obsolete quite yet. This research underscores that while LLMs can be a valuable tool, they're not a replacement for the critical thinking and experience of a human physician, particularly when it comes to interpreting complex medical data and making crucial decisions about patient care. The challenge now lies in refining AI’s ability to handle the numerical side of medicine. Future research will likely focus on bridging this gap, making LLMs even more powerful assistants for medical professionals.