Artificial intelligence language technologies in multilingual healthcare: Grand challenges ahead

2026-05-02

AI Summary

This study addresses critical challenges in deploying AI language technologies in multilingual healthcare settings, where issues such as insufficient clinical safety, uneven cross-lingual performance, hidden errors, and ambiguous accountability hinder equitable and effective clinician–patient communication. Adopting a human-centered AI perspective, the work systematically reviews AI applications in both written and spoken communication as well as agent-based clinical workflows. It integrates methods from human–computer interaction, clinical practice, and implementation science to analyze system capabilities, evaluation frameworks, deployment models, and characteristic failure modes. For the first time, it synthesizes multidisciplinary insights to articulate seven frontier challenges, arguing that progress must extend beyond model performance metrics toward responsible socio-technical design, calibrated human oversight, and cross-domain collaboration. The study offers a directional framework for developing safe, equitable, and trustworthy multilingual AI systems in healthcare.

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.

Problem

• multilingual healthcare
• AI language technologies
• clinical safety
• equitable communication
• reliability

Innovation

• Human-Centered AI
• Multilingual Healthcare
• Large Language Models
• Sociotechnical Design
• Clinical Safety