Training medical professionals requires hands-on experience with diverse patient cases. But access to real patients for training can be limited, expensive, and raise ethical considerations. A new AI framework called EvoPatient is changing the game by creating virtual standardized patients (SPs) powered by large language models (LLMs). These AI patients can simulate a wide range of medical conditions and personalities, allowing doctors to practice their diagnostic skills in a safe and controlled environment. EvoPatient uses a clever “coevolution” strategy where AI doctors and AI patients learn from each other through simulated dialogues. The AI doctors ask questions, and the AI patients respond based on their simulated medical records and personalities. Over time, both the doctors and patients improve their performance, learning to ask more insightful questions and provide more realistic answers, respectively. The system even simulates multidisciplinary consultations, where AI doctors from different specialties can be called in for complex cases. This innovative approach significantly improves the AI patient’s ability to align with specific requirements, providing robust, trustworthy, and accurate responses while minimizing resource consumption. It's like giving medical students a virtual hospital filled with diverse patients, available 24/7. While this technology holds immense promise, there are still challenges to overcome. Ensuring the AI patients fully capture the nuances of human interaction, including non-verbal cues and emotional responses, is an ongoing area of research. Additionally, developing robust evaluation metrics to assess the performance of both AI doctors and patients is crucial for refining the system. EvoPatient represents a significant step toward revolutionizing medical training, offering a scalable and cost-effective solution for developing the next generation of healthcare professionals.