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Summary
This summary is machine-generated.

Large language models (LLMs) show high accuracy in translating radiology reports, with GPT-4 performing best overall. While LLMs improve clarity and readability, medical terminology accuracy needs further development.

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Area of Science:

  • Medical Imaging and Radiology
  • Natural Language Processing
  • Machine Translation

Background:

  • High-quality radiology report translations are crucial for patient care.
  • Limited availability of expert human translators necessitates exploring alternative solutions.
  • Large language models (LLMs) present a promising avenue for automated translation in this domain.

Purpose of the Study:

  • To evaluate the accuracy and quality of various LLMs for radiology report translation.
  • To assess performance across high-resource and low-resource languages.
  • To compare LLM translations against human expert benchmarks.

Main Methods:

  • A dataset of 100 synthetic radiology reports was translated into nine languages by 18 radiologists.
  • Ten LLMs, including GPT-4, Llama 3, and Mixtral, performed automated translations.
  • Translation quality was assessed using BLEU, TER, and chrF++ metrics, alongside qualitative radiologist review.

Main Results:

  • GPT-4 demonstrated superior overall translation quality, particularly for English to German, Greek, Thai, and Turkish.
  • GPT-3.5 excelled in English-to-French, Qwen1.5 in English-to-Chinese, and Mixtral 8x22B in Italian-to-English.
  • LLMs achieved high scores in clarity and readability but showed moderate accuracy in medical terminology.

Conclusions:

  • LLMs offer high accuracy and quality for radiology report translation, though performance varies by model and language pair.
  • Further refinement is needed to enhance the medical terminology accuracy of LLM translations.
  • LLMs represent a valuable tool for overcoming translation barriers in radiology.