Linguistically and culturally competent human interpreters play a crucial role in facilitating language-discordant interpersonal healthcare communication. Traditionally, interpreters work alongside patients and healthcare providers to provide in-person interpreting services. However, problems with access to professional interpreters, including time pressure and a lack of local availability of interpreters, have led to an exploration and implementation of alternative approaches to providing language support. They include the use of communication technologies to access professional interpreters and volunteers but also the application of various language and translation technologies. This chapter offers a critical review of four different approaches, all of which are conceptualised as different types of human-machine interaction: technology-mediated interpreting, crowdsourcing of volunteer language mediators via digital platforms, machine translation, and the use of translation apps populated with pre-translated phrases and sentences. Each approach will be considered in a separate section, beginning with a review of the relevant scholarly literature and main practical developments, followed by a discussion of critical issues and challenges arising. The focus is on dialogic communication and interaction. Technology-assisted methods of translating written texts are not included.

The advent of AI-supported, cloud-based collaborative translation platforms have enabled a new form of online collaborative translation – ‘concurrent translation’ (CT). CT refers to commercial translation performed on such platforms by multiple agents (translators, editors, subject-matter experts etc.) simultaneously, via concurrent access. Although the practice has recently gained more ground, research on CT is scarce. The present article reports on selected key findings of a study that investigates translators’ experiences with CT via a survey of 804 professional translators working in CT mode across different commercial platforms. Despite the affordances such as peer learning, positive competition, speed, flexibility of the volume of work and working time, and reduced responsibility and reduced stress, CT workflow comes with its substantial challenges such as time pressure, negative competition, reduced self-revision and research, all of which result in quality compromised for speed.