In this work, we analyze the performance of publicly available Machine Translation tools for errors in medical translation and test two pre and post-translation interventions for their effectiveness in reducing clinical harm. We focus on two low resourced languages: Amharic and Tigrinya.

We find that MT errors for healthcare most commonly happen when the source sentence includes: medical terminology, synonyms, figurative language, and descriptions of medical procedures. We find that pre and post-translation interventions are not effective in reducing clinical harm if the base translation model performs poorly.

The Chest X-ray Imaging Dataset for Multiple Cardio-respiratory Diseases in Ethiopia (Afro Chest X-ray for short) is a project funded by the LacunaFund whose aim is to close the gap in health disparities by fostering interdisciplinary collaborations that create, expand, or aggregate labeled training and evaluation datasets.

Cardio-respiratory diseases (cardiovascular and respiratory diseases) are recognized as serious, worldwide public health concerns that have remained among the leading causes of death globally. There are not many publicly available datasets from Africa making it difficult to determine whether tools and techniques developed in other geographies are as effective in our context. In this project, we propose to create a labeled chest X-ray dataset for multiple cardio respiratory diseases in Ethiopia. We will publish the dataset as open source. We believe this dataset will stimulate researchers and practitioners in Africa and beyond to push the limits of current methods to adapt them to the African context and build assistive technologies that could empower the scarce radiologists.