P rovision of linguistically appropriate care remains a challenge to achieving health equity.
Language barriers impact 25.6 million limited English proficient (LEP) individuals in the …

Background: Culturally and linguistically diverse groups are often underrepresented in
population-based research and surveillance efforts, leading to biased study results and …

Language barriers between patients and clinicians contribute to disparities in quality of care.
Machine Translation (MT) tools are widely used in healthcare settings, but even small …

Recent research has highlighted privacy as a primary concern for IoT device users.
However, due to the challenges in conducting a large-scale study to analyze thousands of …

Machine learning (ML) models can fail in unexpected ways in the real world, but not all
model failures are equal. With finite time and resources, ML practitioners are forced to …

Recent developments in machine translation (MT) might have led some people to believe
that soon professional translation will not be needed, but most translator trainers are aware …

BACKGROUND AND OBJECTIVES: Hospitalized families who use languages other than
English (LOE) for care encounter unique communication challenges, as do children with …

Objective Linguistic diversity has the potential to create barriers for limited language
proficiency (LLP) patients when navigating the healthcare system. When a verified …

Intimate partner violence (IPV) is a pervasive societal problem that affects millions of people
around the world. IPV perpetrators increasingly weaponize digital technologies like mobile …

Background: The recent advances in machine translation (MT) offer an appealing and low-
cost solution to overcome language barriers in multiple contexts (eg, travelling, cultural …