Machine Translation for Improved Access to Healthcare Information in Remote Zambian Communities

Abstract

Language barriers pose a significant obstacle to achieving national health goals in Zambia. Patients struggle to understand critical medical information that in the process hinder diagnosis, treatment and preventive care. This research proposes a groundbreaking approach to bridge this gap by developing a comprehensive parallel medical linguistic corpora resource and a specialized Machine Translation (MT) system tailored to Zambia’s diverse languages. Zambia’s Eighth National Development Plan prioritizes improved healthcare outcomes. However, widespread language barriers between patients, healthcare providers and public health initiatives create communication gaps. Costly interpreter services are often unavailable, leaving vulnerable populations without access to crucial medical information. This is particularly concerning for mothers seeking maternal care, communities battling disease outbreaks and researchers struggling to include diverse populations in medical studies. This study will address these challenges by developing a next generation MT system and a parallel medical corpus specifically designed for Zambian Low Resourced Languages(LRL). Additionally, the study will pioneer new evaluation metrics tailored to the specific needs of medical translations. The MT system, accessible through a user friendly mobile application, will empower Zambians to access vital health information in their native languages. This will improve communication between patients and healthcare providers that will lead to better diagnoses, treatment plans and overall health outcomes. Furthermore, the research will contribute valuable insights to the broader field of MT, advancing the technology for low-resource languages and specialized domains worldwide.