The rise of AI chatbots in healthcare presents a crucial challenge: how do we ensure they're giving accurate and helpful medical advice? Traditional methods of evaluating these multimodal medical models, like relying on word overlap or human review, are falling short. Human evaluation is costly and slow, while automated metrics often miss the nuances of medical language. A new research paper introduces ACE-M³, an innovative AI-powered solution that acts as an automatic capability evaluator. This open-source model tackles the complex task of judging medical chatbots by using a 'branch-merge' architecture. It breaks down the chatbot's responses into key aspects like medical accuracy, clarity, and empathy, then synthesizes these individual assessments into a final score. This nuanced approach mimics the way medical professionals evaluate information, considering not just factual correctness but also how well the information is communicated. ACE-M³ uses a clever training strategy called Efficient-RTDPO to speed up the learning process without sacrificing performance. The results are promising, with ACE-M³ outperforming existing open-source and even some closed-source multimodal models in evaluation accuracy. While this technology is still in its early stages, it offers a glimpse into a future where AI can play a crucial role in ensuring the safety and reliability of medical chatbots, ultimately helping to improve patient care.