Large Language Model Ability to Translate CT and MRI Free-Text Radiology Reports Into Multiple Languages.

Background High-quality translations of radiology reports are essential for optimal patient care. Because of limited availability of human translators with medical expertise, large language models (LLMs) are a promising solution, but their ability to translate radiology reports remains largely unexplored. Purpose To evaluate the accuracy and quality of various LLMs in translating radiology reports across high-resource languages (English, Italian, French, German, and Chinese) and low-resource languages (Swedish, Turkish, Russian, Greek, and Thai). Materials and Methods A dataset of 100 synthetic free-text radiology reports from CT and MRI scans was translated by 18 radiologists between January 14 and May 2, 2024, into nine target languages. Ten LLMs, including GPT-4 (OpenAI), Llama 3 (Meta), and Mixtral models (Mistral AI), were used for automated translation. Translation accuracy and quality were assessed with use of BiLingual Evaluation Understudy (BLEU) score, translation error rate (TER), and CHaRacter-level F-score (chrF++) metrics. Statistical significance was evaluated with use of paired t tests with Holm-Bonferroni corrections. Radiologists also conducted a qualitative evaluation of translations with use of a standardized questionnaire. Results GPT-4 demonstrated the best overall translation quality, particularly from English to German (BLEU score: 35.0 ± 16.3 [SD]; TER: 61.7 ± 21.2; chrF++: 70.6 ± 9.4), to Greek (BLEU: 32.6 ± 10.1; TER: 52.4 ± 10.6; chrF++: 62.8 ± 6.4), to Thai (BLEU: 53.2 ± 7.3; TER: 74.3 ± 5.2; chrF++: 48.4 ± 6.6), and to Turkish (BLEU: 35.5 ± 6.6; TER: 52.7 ± 7.4; chrF++: 70.7 ± 3.7). GPT-3.5 showed highest accuracy in translations from English to French, and Qwen1.5 excelled in English-to-Chinese translations, whereas Mixtral 8x22B performed best in Italian-to-English translations. The qualitative evaluation revealed that LLMs excelled in clarity, readability, and consistency with the original meaning but showed moderate medical terminology accuracy. Conclusion LLMs showed high accuracy and quality for translating radiology reports, although results varied by model and language pair. © RSNA, 2024 Supplemental material is available for this article.      «

Background High-quality translations of radiology reports are essential for optimal patient care. Because of limited availability of human translators with medical expertise, large language models (LLMs) are a promising solution, but their ability to translate radiology reports remains largely unexplored. Purpose To evaluate the accuracy and quality of various LLMs in translating radiology reports across high-resource languages (English, Italian, French, German, and Chinese) and low-resource lan...     »