Comparative Analysis of Large Language Models in the Interpretation of Gynecologic Pathology Reports
DOI:
https://doi.org/10.65495/eurjimr.2026.14Keywords:
Large Language Models, Patient Education, AI Empathy, Gynecologic PathologyAbstract
This letter evaluates the performance of three large language models in explaining gynecologic pathology reports to patients. Using synthetic cases ranging from benign to malignant diagnoses, the author compares readability, emotional tone, and medical jargon density, highlighting clinically relevant differences in patient-centered communication styles.
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Data Availability Statement
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. All content in this article is original, created by the author. No third-party material (figures, tables, or text excerpts) requiring permission was utilized in this manuscript.
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Copyright (c) 2026 Aslı Karakaşlı (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
