DeepSeek-V3, a recently emerging Large Language Model (LLM), demonstrates outstanding performance in general scene understanding, question-answering (QA), and text generation tasks, owing to its efficient training paradigm and strong reasoning capabilities. In this study, we investigate the dialogue capabilities of DeepSeek-V3 in robotic surgery scenarios, focusing on tasks such as Single Phrase QA, Visual QA, and Detailed Description. The Single Phrase QA tasks further include sub-tasks such as surgical instrument recognition, action understanding, and spatial position analysis. We conduct extensive evaluations using publicly available datasets, including EndoVis18 and CholecT50, along with their corresponding dialogue data. Our comprehensive evaluation results indicate that, when provided with specific prompts, DeepSeek-V3 performs well in surgical instrument and tissue recognition tasks However, DeepSeek-V3 exhibits significant limitations in spatial position analysis and struggles to understand surgical actions accurately. Additionally, our findings reveal that, under general prompts, DeepSeek-V3 lacks the ability to effectively analyze global surgical concepts and fails to provide detailed insights into surgical scenarios. Based on our observations, we argue that the DeepSeek-V3 is not ready for vision-language tasks in surgical contexts without fine-tuning on surgery-specific datasets.

翻译：DeepSeek-V3作为新兴的大型语言模型，凭借其高效的训练范式和强大的推理能力，在通用场景理解、问答及文本生成任务中展现出卓越性能。本研究探究了DeepSeek-V3在机器人手术场景中的对话能力，重点关注短语问答、视觉问答及细节描述等任务。其中短语问答任务进一步包含手术器械识别、动作理解与空间位置分析等子任务。我们使用公开数据集（包括EndoVis18与CholecT50）及其对应对话数据进行了广泛评估。综合评估结果表明：在特定提示下，DeepSeek-V3在手术器械与组织识别任务中表现良好；然而该模型在空间位置分析方面存在显著局限，且难以准确理解手术动作。此外，研究发现通用提示下的DeepSeek-V3缺乏分析全局手术概念的能力，无法对手术场景提供细节洞察。基于实验观察，我们认为未经手术专用数据集微调的DeepSeek-V3尚不具备处理手术场景视觉语言任务的能力。