The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review

Abstract

Background Culturally and linguistically diverse groups are often underrepresented in population-based research and surveillance efforts, leading to biased study results and limited generalizability. These groups, often termed “hard-to-reach,” commonly encounter language barriers in the public health (PH) outreach material and information campaigns, reducing their involvement with the information. As a result, these groups are challenged by 2 effects: the medical and health knowledge is less tailored to their needs, and at the same time, it is less accessible for to them. Modern machine translation (MT) tools might offer a cost-effective solution to PH material language accessibility problems. Objective This scoping review aims to systematically investigate current use cases of MT specific to the fields of PH and epidemiology, with a particular interest in its use for population-based recruitment methods. Methods PubMed, PubMed Central, Scopus, ACM Digital Library, and IEEE Xplore were searched to identify articles reporting on the use of MT in PH and epidemiological research for this PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews)–compliant scoping review. Information on communication scenarios, study designs and the principal findings of each article were mapped according to a settings approach, the World Health Organization monitoring and evaluation framework and the service readiness level framework, respectively. Results Of the 7186 articles identified, 46 (0.64%) were included in this review, with the earliest study dating from 2009. Most of the studies (17/46, 37%) discussed the application of MT to existing PH materials, limited to one-way communication between PH officials and addressed audiences. No specific article investigated the use of MT for recruiting linguistically diverse participants to population-based studies. Regarding study designs, nearly three-quarters (34/46, 74%) of the articles provided technical assessments of MT from 1 language (mainly English) to a few others (eg, Spanish, Chinese, or French). Only a few (12/46, 26%) explored end-user attitudes (mainly of PH employees), whereas none examined the legal or ethical implications of using MT. The experiments primarily involved PH experts with language proficiencies. Overall, more than half (38/70, 54% statements) of the summarizing results presented mixed and inconclusive views on the technical readiness of MT for PH information. Conclusions Using MT in epidemiology and PH can enhance outreach to linguistically diverse populations. The translation quality of current commercial MT solutions (eg, Google Translate and DeepL Translator) is sufficient if postediting is a mandatory step in the translation workflow. Postediting of legally or ethically sensitive material requires staff with adequate content knowledge in addition to sufficient language skills. Unsupervised MT is generally not recommended. Research on whether machine-translated texts are received differently by addressees is lacking, as well as research on MT in communication scenarios that warrant a response from the addressees.

Performance of machine translators in translating French medical research abstracts to English: A comparative study of DeepL, Google Translate, and CUBBITT

“…MTs are increasingly used in medicine, particularly to translate electronic medical records and improve patient care [3,[7][8][9][10][11][12][13][14][15]. Current evidence also suggests that they are relatively reliable for extracting data from non-English articles in systematic reviews [16,17].…”

“…Indeed, thanks to neural networks, the quality of translation has greatly improved in the last decades [4][5][6] and they do not require advanced computer skills. They are also used in medicine, for example to translate electronic medical records and to improve patient management in clinical practice, with mixed results [3,[7][8][9][10][11][12][13][14][15]. For example, Taira et al assessed the use of Google Translate for translating commonly used Emergency Department discharge instructions into seven languages [9].…”

Machine Translation for Multilingual Cancer Patient Education: Bridging Languages, Navigating Challenges

“…The proposition of a hybrid model, combining machine translation with human post-editing, is a step towards mitigating these risks [4]. However, this approach also underscores the indispensable value of human expertise in translation, particularly for materials with significant clinical ramifications.…”