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.

翻译：中医理论建立在意象思维之上，其医学原理及诊疗逻辑通过隐喻与转喻构建。然而，现有英译多依赖字面直译，导致目标语读者难以重构背后的概念网络并将其应用于临床实践。本研究采用人机协同框架，选取中医典籍《黄帝内经》中四段理论核心文本。通过基于提示的认知支架，引导DeepSeek V3.1识别源文本中的隐喻与转喻，并在翻译中传递理论内涵。在评估阶段，通过提示指令使ChatGPT 5 Pro与Gemini 2.5 Pro模拟三类真实读者，对人工翻译、基线模型翻译及提示调整后的翻译在五个认知维度进行评分，辅以结构化访谈与解释现象学分析。结果显示，经提示调整的大语言模型翻译在五个维度均表现最佳，且具有较高的跨模型与跨角色一致性。访谈主题揭示了人机翻译差异、隐喻与转喻传递的有效策略以及读者的认知偏好。本研究为中医等概念密集的古籍翻译提供了一条认知导向、高效且可复现的人机协同方法路径。