Imagine a team of AI doctors collaborating on a diagnosis, much like specialists in a hospital. That’s the idea behind a new approach to medical decision-making using large language models (LLMs). Researchers have developed a system called MDAgents, which dynamically assembles teams of specialized AI agents based on the complexity of a medical case. For simple cases, a single AI “general practitioner” might be enough. But for complex cases involving multiple symptoms or requiring interdisciplinary expertise, MDAgents assembles a tailored team of AI specialists, such as neurologists, oncologists, or radiologists. These AI agents work together, analyzing patient data and engaging in virtual discussions to arrive at a more accurate diagnosis. The system even incorporates up-to-date medical knowledge to stay current with the latest research. Initial tests show that these AI teams can outperform single LLMs and even static groups of AI agents, particularly in complex cases. While still in its early stages, this research offers a glimpse into a future where AI could play a crucial role in supporting human doctors, improving diagnostic accuracy, and ultimately leading to better patient outcomes. Future work will focus on incorporating real-time feedback from human doctors to refine the system's accuracy and ensure it aligns with established medical practices.