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  3. Medcod: Enhancing English-to-spanish Medical Translation Of Large Language Models Using Enriched Chain-of-dictionary Framework.
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  3. Medcod: Enhancing English-to-spanish Medical Translation Of Large Language Models Using Enriched Chain-of-dictionary Framework.

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MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary

Md Shahidul Salim1,2, Lian Fu3, Arav Adikesh Ramakrishnan3

  • 1Center for Healthcare Organization and Implementation Research, VA Bedford Health Care.

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|February 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed MedCOD, a framework enhancing English-to-Spanish medical translation by integrating structured medical knowledge into large language models (LLMs). This approach significantly boosts translation quality across various models.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Accurate medical translation is crucial for global healthcare access.
  • Large Language Models (LLMs) show promise but struggle with domain-specific medical terminology.
  • Existing translation methods lack robust integration of structured medical knowledge.

Purpose of the Study:

  • To introduce MedCOD (Medical Chain-of-Dictionary), a novel hybrid framework for improved English-to-Spanish medical translation.
  • To enhance LLMs by integrating structured domain knowledge from UMLS and LLM-KB paradigms.
  • To evaluate the effectiveness of MedCOD in improving translation quality across multiple open-source LLMs.

Main Methods:

  • Constructed a parallel corpus of 2,999 English-Spanish MedlinePlus articles.
  • Developed a 100-sentence test set with structured medical contexts.
  • Employed structured prompting with multilingual variants, synonyms, and UMLS definitions.
  • Utilized LoRA-based fine-tuning on four open-source LLMs (Phi-4, Qwen2.5-14B, Qwen2.5-7B, LLaMA-3.1-8B).

    • Main Results:

    • MedCOD significantly improved translation quality across all evaluated LLMs.
    • Phi-4 with MedCOD and fine-tuning achieved superior BLEU (44.23), chrF++ (28.91), and COMET (0.863) scores.
    • Both MedCOD prompting and model adaptation independently enhanced performance, with combined use yielding maximal gains.
    • Performance surpassed strong baseline models like GPT-4o and GPT-4o-mini.

    Conclusions:

    • Structured knowledge integration via MedCOD substantially enhances LLM performance for medical translation.
    • The MedCOD framework offers a viable strategy for improving the accuracy and reliability of medical language translation.
    • This approach holds significant potential for advancing AI applications in global health communication.