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RadTranslateGPT: An Improved AI-Based System for Translation and Simplification of Structured Radiology Reports.
Praneet Khanna1, Aneesh Mazumder2, Joel Kevin Raj Samuel3
1Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center, Mass General Brigham, Boston, Massachusetts; University of Missouri-Kansas City School of Medicine, Kansas City, Missouri.
Journal of the American College of Radiology : JACR
|April 12, 2026
Summary
GPT-o1 simplifies and translates emergency radiology reports effectively for limited English proficiency (LEP) patients. This large language model shows promise in improving health literacy for diverse populations.
Area of Science:
- Medical Informatics
- Natural Language Processing
- Health Communication
Background:
- Radiology reports contain complex medical jargon.
- Limited English proficiency (LEP) patients face comprehension barriers.
- Need for patient-friendly, multilingual report interpretation exists.
Purpose of the Study:
- Evaluate GPT-o1 for simplifying emergency radiology reports.
- Assess GPT-o1's translation accuracy into Spanish, Arabic, and Mandarin.
- Compare GPT-o1 performance against Google Translate.
Main Methods:
- Selected 30 de-identified emergency radiology reports.
- Radiologists evaluated GPT-o1 simplified reports (Phase 1).
- Medical interpreters assessed GPT-o1 and Google Translate outputs (Phase 2).
- Used a 5-point Likert scale for ratings.
Main Results:
- 76.9% of reports rated 'Extremely/Very accurate'.
- 45.6% rated 'Very Clear'; 98.9% rated 'useful'.
- GPT-o1 significantly outperformed Google Translate in accuracy and comprehensibility (p < .001).
Conclusions:
- GPT-o1 generates high-quality, simplified, and translated radiology reports.
- Findings suggest large language models (LLMs) can enhance health literacy for LEP patients.
- Further validation is required prior to clinical integration.
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