Machine translation in healthcare

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

Machine translation has enormous potential to improve communication across language barriers in the healthcare setting. We first explain what machine translation (MT) is, and why it has the potential to be useful in the health domain. We provide a brief account of the history of machine translation, covering the three main paradigms (rule-based, statistical and neural) and describe where the current state-of-the-art lies in relation to the goal of ‘fully automatic high-quality MT’. We identify different models of usage for MT (assimilation, post-editing) link these models with evaluation methods, and discuss their application to translation in health. We then describe a selection of research projects applying MT to the health domain, focusing most attention on two that we have personal acquaintance with: HimL and MedicalMT. Finally we present an outlook for the future of MT in the health domain.
Original languageEnglish
Title of host publicationThe Routledge Handbook of Translation and Health
EditorsŞebnem Susam-Saraeva, Eva Spišiaková
Place of PublicationLondon
PublisherRoutledge
Chapter7
Number of pages22
Edition1
ISBN (Electronic)9781003167983
DOIs
Publication statusPublished - 10 May 2021

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