Impact of Fine-Tuned Machine Translation on Medical Documentation Translation and Client Satisfaction

Abstract

Aim: The aim of this study was to assess the impact of Fine-Tuned Machine Translation (FTMT) models, followed by Machine Translation Post-Editing (MTPE), on the translation quality and client satisfaction in medical documentation. Materials and Methods: The research analyzed 733,632 words across 317 projects completed in 2023 by Lingrowth, a medical and life science translation service provider. These projects involved translations from 16 source languages to 34 target languages. Document types included Instructions for Use (IFU), Investigator Brochures (IB), Summary of Product Characteristics (SmPC), Clinical Study Reports (CSR), and Informed Consent Forms (ICF). The projects were categorized into two groups for comparison: translations performed by human translators using Translation Memory (TM) and those processed by FTMT followed by MTPE. The primary metrics for evaluation were the number of post-project finalization requests and instances of severe negative feedback from clients. Results: It was observed that human-translated projects had a higher frequency of post-project finalization requests, whereas MTPE projects after FTMT exhibited a marginally higher rate of severe negative feedback. However, statistical analysis indicated that these differences were not significant, suggesting that the introduction of FTMT models with subsequent MTPE does not adversely affect the overall quality of medical document translations. Conclusion: The study concludes that FTMT models, when supplemented by MTPE, are com-parable in effectiveness to traditional human translations in the context of medical documentation. This highlights the potential of integrating FTMT in translation workflows without compromising translation quality or client satisfaction.