Multi-Method Validation of Large Language Model Medical Translation Across High- and Low-Resource Languages
Language barriers affect 27.3 million U.S. residents with non-English language preference, yet professional medical translation remains costly and often unavailable. We evaluated four frontier large language models (GPT-5.1, Claude Opus 4.5, Gemini 3 Pro, Kimi K2) translating 22 medical documents into 8 languages spanning high-resource (Spanish, Chinese, Russian, Vietnamese), medium-resource (Korean, Arabic), and low-resource (Tagalog, Haitian Creole) categories using a five-layer validation framework. Across 704 translation pairs, all models achieved high semantic preservation (LaBSE greater than 0.92), with no significant difference between high- and low-resource languages (p = 0.066). ...
| Authors | Chukwuebuka Anyaegbuna, Eduardo Juan Perez Guerrero, Jerry Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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