中医理论基于意象思维，通过隐喻和转喻构建医学原理与诊疗逻辑。现有英文翻译多依赖直译，导致目标语读者难以重建概念网络并应用于临床。本研究采用人机协同框架，从《黄帝内经》选取四段基础理论文本，通过提示驱动的认知导引，引导DeepSeek V3.1识别原文中的隐喻和转喻并在翻译中传递理论。
评估阶段，研究者使用提示指令让ChatGPT 5 Pro和Gemini 2.5 Pro模拟三类真实读者，对人工翻译、基线模型翻译和提示调整翻译在五个认知维度上评分，随后进行结构化访谈和解释现象学分析。结果表明，提示调整的LLM翻译在所有五个维度上表现最优，且具有高跨模型和跨角色一致性。
访谈主题揭示了人工与机器翻译的差异、隐喻和转喻传递的有效策略以及读者的认知偏好。本研究为《黄帝内经》等古代概念密集型文本的翻译提供了一条认知性、高效且可复制的人机协同方法论路径，关键数据包括五维度评分结果及跨模型一致性验证。
Traditional Chinese Medicine (TCM) theory is built on imagistic thinking, in which medical principles and diagnostic and therapeutic logic are structured through metaphor and metonymy. However, existing English translations largely rely on literal rendering, making it difficult for target-language readers to reconstruct the underlying conceptual networks and apply them in clinical practice. This study adopted a human-in-the-loop (HITL) framework and selected four passages from the medical canon Huangdi Neijing that are fundamental in theory. Through prompt-based cognitive scaffolding, DeepSeek V3.1 was guided to identify metaphor and metonymy in the source text and convey the theory in translation. In the evaluation stage, ChatGPT 5 Pro and Gemini 2.5 Pro were instructed by prompts to simulate three types of real-world readers. Human translations, baseline model translations, and prompt-adjusted translations were scored by the simulated readers across five cognitive dimensions, followed by structured interviews and Interpretative Phenomenological Analysis (IPA). Results show that the prompt-adjusted LLM translations perform best across all five dimensions, with high cross-model and cross-role consistency. The interview themes reveal differences between human and machine translation, effective strategies for metaphor and metonymy transfer, and readers' cognitive preferences. This study provides a cognitive, efficient, and replicable HITL methodological pathway for the translation of ancient, concept-dense texts such as TCM.
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