RadTranslateGPT: An Improved AI-Based System for Translation and Simplification of Structured Radiology Reports.

Abstract

Radiology reports often contain complex medical jargon that can be difficult for patients to understand, especially those with limited English proficiency. With increased access to electronic health records, there is a need for patient-friendly and multilingual interpretation of radiology reports. The aim of this study was to evaluate the performance of GPT-o1 in simplifying and translating emergency radiology reports into Spanish, Arabic, and Mandarin and to compare it with Google Translate. Thirty deidentified emergency radiology reports were selected from an institutional database. Phase 1 involved the evaluation of the GPT-o1-simplified reports by three board-certified emergency radiologists. Phase 2 involved nine medical interpreters (three per language) assessing both GPT-o1 and Google Translate outputs. Both groups rated outputs on a 5-point, Likert-type scale. There were a total of 90 evaluations of the simplified radiology reports. Sixty-nine the reports (76.9%) were rated as "extremely accurate" or "very accurate," 41 (45.6%) were rated as "very clear," and 89 (98.9%) were marked as "useful" for patient comprehension. GPT-o1 achieved significantly higher translational accuracy (median, 4.0 vs 3.0; P < .001) and higher language register scores and was rated more comprehensible than Google Translate in all languages tested. GPT-o1 generated simplified, patient-friendly radiology reports and also produced high-quality translations into the three languages tested; however, these findings should be interpreted in the context of this pilot study with a limited sample size. These findings suggest that large language models could serve as tools to enhance health literacy for limited English proficiency populations. Further validation of these large language models is needed before clinical integration.