Google Translate Error Analysis for Mental Healthcare Information:
  Evaluating Accuracy, Comprehensibility, and Implications for Multilingual
  Healthcare Communication
Jaleh Delfani Constantin Orasan Hadeel Saadany Ozlem Temizoz Eleanor Taylor-Stilgoe Diptesh Kanojia Sabine Braun Barbara Schouten
This study explores the use of Google Translate (GT) for translating mental
healthcare (MHealth) information and evaluates its accuracy, comprehensibility,
and implications for multilingual healthcare communication through analysing GT
output in the MHealth domain from English to Persian, Arabic, Turkish,
Romanian, and Spanish. Two datasets comprising MHealth information from the UK
National Health Service website and information leaflets from The Royal College
of Psychiatrists were used. Native speakers of the target languages manually
assessed the GT translations, focusing on medical terminology accuracy,
comprehensibility, and critical syntactic/semantic errors. GT output analysis
revealed challenges in accurately translating medical terminology, particularly
in Arabic, Romanian, and Persian. Fluency issues were prevalent across various
languages, affecting comprehension, mainly in Arabic and Spanish. Critical
errors arose in specific contexts, such as bullet-point formatting,
specifically in Persian, Turkish, and Romanian. Although improvements are seen
in longer-text translations, there remains a need to enhance accuracy in
medical and mental health terminology and fluency, whilst also addressing
formatting issues for a more seamless user experience. The findings highlight
the need to use customised translation engines for Mhealth translation and the
challenges when relying solely on machine-translated medical content,
emphasising the crucial role of human reviewers in multilingual healthcare
communication.