Artificial intelligence language technologies in multilingual healthcare: Grand challenges ahead
Vicent Briva-Iglesias
Why It Matters
What makes this one worth your time
Understanding the limitations and challenges of AI language technologies is crucial for improving patient safety and communication in diverse healthcare environments.
This review identifies critical challenges in deploying AI language technologies in healthcare settings.
Summary
The paper reviews the integration of AI language technologies in multilingual healthcare, highlighting challenges in communication safety and equity, and proposes seven grand challenges for future research.
Key contributions
- Synthesis of recent peer-reviewed evidence on AI language technologies in healthcare.
- Identification of seven grand challenges for future research and deployment.
- Examination of capabilities and recurrent errors through a Human-Centered AI lens.
Notable insights
- The paper emphasizes the need for accountable sociotechnical design in AI language technologies.
- It highlights the variability in performance across different languages and tasks, which is often overlooked.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.01441v1 Announce Type: new 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.