Abstract
Healthcare systems in the United States are now mandated to provide patients with immediate access to medical results. Access to complex results prior to appropriate clinical follow-up may cause confusion and fear, especially in populations with limited health literacy and English language proficiency. In this study, we demonstrate a use case of large language models (LLMs) to simplify and translate complex medical information for patients. We collected a consecutive series of brain MRI (N = 200), head CT (N = 100), and spine MRI (N = 200) reports, prompting a GPT-4o model to simplify these reports to a middle-school reading level and translate a subset of them to Spanish. Critically, two independent neuroradiology raters compared the simplified report impressions to the original report impressions, and found low rates of hallucinations, omissions, and imprecisions. The reading grade of the report impressions decreased from approximately college to approximately middle-school level, and readability improved across several text complexity metrics. Additionally, simplified impressions were classified as more neutral and less fearful. Machine generated translations of simplified impressions were statistically indistinguishable from manual translations as assessed by certified interpreters. In sum, we present a scalable, patient-friendly approach to promote healthcare literacy and engagement.
Acknowledgements
We thank Dr. Hasan Zaidi, Sigfredo Salguero, Yilu Ma, Dr. Anne Billot, and Dylan Loomis for helpful discussion.
Funding
This work was also supported by the Massachusetts Medical Society Information Technology Award (W.S.).
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The authors declare no competing interests.
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This research was performed in accordance with the Declaration of Helsinki. All research activities were approved by the Institutional Review Board (IRB) at Mass General Brigham (MGB). MGB IRB protocol number: 2024P002013. Clinical trial number: not applicable.
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Sun, W., Patil, A., Vicent, M.A. et al. Patient-friendly simplification and translation of neuroradiology impressions using artificial intelligence. Sci Rep (2026). https://doi.org/10.1038/s41598-026-48030-3
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DOI: https://doi.org/10.1038/s41598-026-48030-3
