*What is this study about?* This study introduces *MDAgents*, a system that helps Large Language Models (LLMs) work together in a smart way to make better medical decisions. Instead of using just one AI or a fixed group, MDAgents adjusts the team based on how complex a medical problem is—just like how doctors work alone for simple cases but collaborate for complicated ones. *What did the study find?* 1. Better Performance: - MDAgents performed better than previous AI methods in 7 out of 10 medical tests. - Accuracy improved by up to 4.2% compared to older methods. - When combined with expert reviews and external medical knowledge, accuracy increased by 11.8%. 2. How It Works: - Step 1: Check the Complexity – The system first decides if a case is simple, moderate, or complex. - Step 2: Recruit AI Experts – A single AI handles easy cases, while teams of AI “specialists” are assigned for harder cases. - Step 3: Analyze and Discuss – The AI agents work together to combine medical knowledge and find the best answer. - Step 4: Make the Final Decision – The system picks the most accurate answer and provides a report. 3. Why MDAgents is Better: - Smarter teamwork: Instead of a fixed AI setup, MDAgents adjusts based on the situation. - Saves time and effort: It reduces unnecessary steps, making decisions faster and more accurate. - Handles different types of medical information: It can work with text, images, and medical literature to make better diagnoses. *Why does this matter?* MDAgents mimics how real doctors make decisions, making AI more useful in healthcare. By adjusting to different cases, it improves medical accuracy while avoiding unnecessary AI processing. This makes AI a more reliable assistant for doctors and healthcare professionals. https://arxiv.org/abs/2404.15155