Artificial intelligence language technologies in multilingual healthcare: Grand challenges ahead

TL;DR

AI language technologies are increasingly used in multilingual healthcare, but challenges remain in ensuring safety, equity, and accountability across diverse languages and workflows.

Contribution

This review synthesizes recent evidence on AI language tech in healthcare and proposes seven grand challenges for future research and deployment.

Findings

Performance varies across languages and tasks.

Efficiency gains can hide errors and reduce traceability.

Progress requires better models and accountable sociotechnical design.

Abstract

AI language technologies (AILTs), increasingly enabled by large language models (LLMs), are becoming embedded in multilingual healthcare workflows for translation, rewriting, documentation, interpreting, and messaging in language-discordant settings. Yet fluent output is not the same as clinically safe or equitable communication: performance varies across languages, accents, tasks, and workflows, and efficiency gains can hide errors, reduce traceability, and shift responsibility across clinicians, translators, interpreters, and health systems. This narrative review synthesises recent peer-reviewed evidence across written communication, spoken communication, and emerging agentic workflows. Using the Human-Centered AI Language Technology (HCAILT) lens, it examines capabilities, evaluation practices, implementation patterns, and recurrent errors through reliability, safety culture, and trustworthiness. We identify key convergences and contradictions in the literature and propose seven grand challenges for the next phase of research and deployment. Progress, we argue, requires not only better models but also accountable sociotechnical design, calibrated human oversight, and stronger collaboration across MT/NLP, translation studies, HCI, clinical practice, implementation science, and policy.