Bridging the Gap: Challenges in Evaluating AI Tools for Interpretation and Translation in Healthcare

Abstract

Language barriers remain a persistent challenge in the U.S. healthcare system, affecting approximately 26 million individuals. Artificial intelligence (AI) tools for interpretation and translation are emerging as potential solutions to address interpreter shortages, yet their evaluation remains underdeveloped and inconsistent. This article explores the complexities of evaluating AI-driven interpretation tools, particularly for spoken language in clinical contexts. Drawing from both automated and human-centric evaluation frameworks, we critique current methodologies such as BLEU scores and discuss the importance of adequacy, fluency and meaning preservation in the evaluation process. The piece emphasizes the need for interdisciplinary collaboration and standardization in measuring the effectiveness and safety of these technologies in real-world healthcare environments.