Evaluating Artificial Intelligence for Language Translation of Health-related Knowledge Mobilization Resources: A Randomized Controlled Trial and Qualitative Study

Background

Knowledge mobilization (KM) aims to ensure that research and evidence are effectively shared, applied and integrated into decision-making processes [1, 2]. KM resources serve different end-users (e.g., policymakers, healthcare providers, patients, caregivers) and can take various forms depending on their target audience (e.g., resources for patients include plain language summaries, videos, or infographics). By translating complex health information into user-friendly formats and evaluating dissemination strategies, this approach enhances awareness, engagement, and accessibility. In the context of child health, KM helps parents and caregivers access, understand, and use research findings to make informed healthcare decisions [3].

Our team has successfully co-created KM resources with families, integrating research, lived experience, and art to resonate with parents [4-10]. Our resources reflect diverse identities, including gender, family structure, ability, appearance, and socioeconomic status. The investigators have extensively evaluated accessible formats (e.g., video, interactive infographics) and effective dissemination strategies (e.g., social media) to maximize engagement [11-13]. Interactive infographics offer broad accessibility, easily shared across various platforms (e.g., websites, social media) to reach diverse audiences, regardless of geographic location (urban, rural, remote) or socioeconomic background (e.g., gender, race, culture, education, income) [14].

Communication challenges due to language barriers (specifically understanding or accessing healthcare information) have been shown to impact quality of care and lead to poor health outcomes [15]. While KM resources are meant to be widely accessible, they can be challenging for those whose first language is not English. According to the 2021 Canadian census, immigrants make up 23% of the population. This proportion is growing and is expected to reach 29-34% by 2041 [16, 17]. The proportion in Alberta is consistent with Canada overall at 23%, with the top places of birth of immigrants being the Philippines, India, and China. Therefore, the communities of interest in this proposal also represent common non-English languages spoken at home in Alberta: Mandarin, Punjabi, Tagalog, Urdu [18].

With increasing diversity in Alberta, the complexities associated with supporting groups with different ethnocultural backgrounds also increases. In situations where immigrants may face challenges in adjusting to new sociopolitical environments and health systems, KM in health is critical to address their knowledge and information needs [19, 20]. Translating KM resources into multiple languages can mitigate disparities in healthcare access and quality, ensuring that immigrants receive the information they need to navigate and engage with the health system effectively [15]. However, language translation of KM resources by a professional (i.e., human) translator can be resource intensive, costly, and time consuming. Artificial intelligence (AI) applications (e.g., ChatGPT, DeepL, Google Translate) have the potential for more efficient translation [21]. Yet, there may be disadvantages with using AI for translation. For instance, translations involve not only the literal meaning of words but also the cultural implications and social connotations attached to them [22, 23]. AI may lack the ability to recognize cultural subtleties, which could result in insensitive or inappropriate interpretations [22]. Nevertheless, as technology rapidly advances, AI is likely to be used for healthcare communications to enhance access and quality of care for those who are not proficient in English [24]. Empirical assessments of AI translation are therefore essential for understanding its capabilities and limitations.