The field of vision-language understanding is rapidly advancing in medical domains, with a focus on developing large language models (LLMs) that can effectively reason and analyze complex clinical scenarios. Recent studies have investigated the application of LLMs in robotic-assisted surgery, anesthesiology, and surgical artificial intelligence, demonstrating their potential to augment medical decision-making. However, these studies also highlight the need for domain-specific validation, interpretability safeguards, and confidence metrics to ensure reliability in real-world applications. Notably, the use of in-context learning and adaptability has shown promise in improving the performance of vision-language models in surgical AI. Overall, the field is moving towards developing more sophisticated and specialized models that can effectively tackle the complexities of medical domains. Noteworthy papers include:

• A study on DeepSeek-V3, which demonstrates its limitations in spatial position analysis and understanding surgical actions.
• An in-depth analysis of DeepSeek R1's medical reasoning, which reveals recurring flaws such as anchoring bias and insufficient exploration of alternatives.
• The introduction of AnesBench, a cross-lingual benchmark for evaluating LLM reasoning in anesthesiology, which provides valuable insights into the factors influencing model performance.