Related Experiment Video
Updated: Jul 5, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Evaluation of Machine Translation Accuracy Focused on the Adverse Event Terminology for Medical Devices
Ayako Yagahara1, Masahito Uesugi2, Hideto Yokoi3
1Hokkaido University of Science, Sapporo, Japan.
Abstract:
The purpose of this study was to evaluate the accuracy of deep neural machine translation focused on medical device adverse event terminology. 10 models were obtained, and their English-to-Japanese translation accuracy was evaluated using quantitative and qualitative measures. No significant difference was found in the quantitative index except for a few pairs. In the qualitative evaluation, there was a significant difference and googletrans and GPT-3 were regarded as useful models.
More Related Videos
Related Concept Videos
Improving Translational Accuracy
Guidelines for Nursing Documentation I
Factual:
The following points emphasize the significance of upholding accurate and unbiased documentation in healthcare.
Leaky Scanning
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Hazard Ratio
For example, in a clinical trial...

