Advancing health equity: evaluating AI translations of kidney donor information for Spanish speakers - Garcia Valencia, Oscar A.; Thongprayoon, Charat; Jadlowiec, Caroline C.; Mao, Shennen A.; Leeaphorn, Napat; Budhiraja, Pooja; Khoury, Nadeen; Pham, Justin H.; Craici, Iasmina M.; Gonzalez Suarez, Maria L.; Cheungpasitporn, Wisit (Frontiers in Public Health, 2025)
BackgroundHealth equity and access to essential medical information remain significant challenges, especially for the Spanish-speaking Hispanic population, which faces barriers in accessing living kidney donation opportunities. ChatGPT, an AI language model with sophisticated natural language processing capabilities, has been identified as a promising tool for translating critical health information into Spanish. This study aims to assess ChatGPT’s translation efficacy to ensure the information provided is accurate and culturally relevant.MethodsThis study utilized ChatGPT versions 3.5 and 4.0 to translate 27 frequently asked questions (FAQs) from English to Spanish, sourced from Donate Life America’s website. The translated content was reviewed by native Spanish-speaking nephrologists using a standard rubric scale (1–5). The assessment focused on linguistic accuracy and cultural sensitivity, emphasizing retention of the original message, appropriate vocabulary and grammar, and cultural relevance.ResultsThe mean linguistic accuracy scores were 4.89 ± 0.32 for GPT-3.5 and 5.00 ± 0.00 for GPT-4.0 (p = 0.08). The percentage of excellent-quality translations (score = 5) in linguistic accuracy was 89% for GPT-3.5 and 100% for GPT-4.0 (p = 0.24). The mean cultural sensitivity scores were 4.89 ± 0.32 for both GPT-3.5 and GPT-4.0 (p = 1.00). Similarly, excellent-quality translations in cultural sensitivity were achieved in 89% of cases for both versions (p = 1.00).ConclusionChatGPT 4.0 demonstrates strong potential to enhance health equity by improving Spanish-speaking Hispanic patients’ access to LKD information through accurate and culturally sensitive translations. These findings highlight the role of AI in mitigating healthcare disparities and underscore the need for integrating AI-driven tools into healthcare systems. Future efforts should focus on developing accessible platforms and establishing guidelines to maximize AI’s impact on equitable healthcare delivery and patient education.