Bridging language gaps in healthcare: a systematic review of the practical implementation of neural machine translation technologies in clinical settings

OBJECTIVES: Effective communication is crucial in healthcare, and for patients with a non-English language preference (NELP), professional interpreters are recognized as the gold standard in supporting bidirectional communication. However, interpreters are not always readily available, prompting the exploration of other options for translation and interpretation. The recent developments in artificial intelligence-based neural network translation tools, namely neural machine translation (NMT) may enable robust interpretation and translation. MATERIALS AND METHODS: We conducted a systematic review (SR) to evaluate the literature on NMT for this purpose. We did a comprehensive search of several databases with guidance from a professional librarian. The search was limited to the year 2000 onwards and English language. Title and abstract screening and full-text review were independently conducted by two reviewers with conflicts resolved by a third reviewer. RESULTS: 2867 studies were identified with 10 studies included in the final analysis. Among these, six evaluated interpretation in real or simulated clinical settings and four examined translation of discharge materials. Google Translate and ChatGPT were assessed in several studies. Accuracy differed by language, with low-resource languages performing worse. DISCUSSION: NMT technologies in healthcare have several advantages including broad language accessibility and potential cost savings for institutions. Despite improved accuracy of these novel tools, due to possible critical errors NMT tools are not yet ready for widespread clinical use. CONCLUSION: Future studies should focus on optimizing evaluation methods as well as how best to integrate these technologies into real-time clinical settings.