Lost In Translation? Evaluating AI Solutions For Language Inclusivity In Global Surgery Literature
Background: Language barriers significantly exacerbate global surgical disparities by restricting access to essential knowledge. AI translation tools offer scalable solutions to address these barriers, enhancing equity through improved accessibility to global surgery literature. Effective AI translation must achieve accuracy while preserving original formatting and readability. Methods: We evaluated three leading AI translation platforms—DeepL, ChatGPT, and Google Translate—by translating four peer-reviewed global surgery articles from English to Spanish. Translation quality was assessed quantitatively using validated computational metrics ordered by their correlation with human judgment: COMET, BLEURT, BERTScore, METEOR, and BLEU. Qualitative evaluations considered readability, terminology accuracy, and preservation of academic formatting. Results: DeepL demonstrated superior performance in translation quality, achieving the highest scores in COMET (92.0), BLEURT (89.8), and BERTScore (93.3), which represent strong indicators of human-like translation accuracy. ChatGPT closely followed, showing competitive scores in COMET (91.5) and equal BLEURT performance (89.8). Google Translate ranked lowest among the evaluated platforms across most metrics, with scores notably lower in COMET (90.8), BLEURT (89.0), and BERTScore (92.5). While all three platforms provided high functional capacity, DeepL most consistently retained original document formatting, enhancing readability and preserving academic presentation integrity. Conclusion: AI-powered translation, particularly via DeepL, demonstrates significant potential for reducing language-based inequities in global surgery. Effective translations must not only achieve semantic accuracy but also retain document functionality and formatting fidelity. Prioritizing robust, human-aligned translation metrics can help global surgery initiatives choose the most suitable AI tools, thus advancing equity through enhanced language inclusion.
Authors
- Katayoun Madani (ORCID: https://orcid.org/0000-0001-8665-2859)
- Sofia Wagemaker
- Brenda Feres (ORCID: https://orcid.org/0000-0002-6523-8891)
- Christopher Dodgion (ORCID: https://orcid.org/0000-0002-5789-7731)
- SP Law
- Habib Alagbo
- Chandler Hinson
- Fatima Qamar
- Neharaj Pitla
- Ramitha Ruwanpathirana
Publication Details
- Published
- 2025-05-04
- DOI
- https://doi.org/10.52648/icigs.1003_117
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00