AI language tech faces grand challenges in multilingual healthcare safety

A new review paper highlights significant challenges in applying AI language technologies within multilingual healthcare settings. While these technologies, powered by large language models, offer potential benefits in translation, documentation, and communication, their current performance is inconsistent across languages and can obscure critical errors. The paper argues that achieving safe and equitable healthcare communication requires not only improved AI models but also careful sociotechnical design, human oversight, and interdisciplinary collaboration. AI

IMPACT Highlights the need for robust safety and equity measures when deploying AI language models in critical healthcare applications.

RANK_REASON This is a review paper published on arXiv discussing challenges and future research directions in AI language technologies for healthcare. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• AI language technologies
• AILTs
• arXiv
• HCAILT
• Human-Centered AI Language Technology
• large language models
• LLMs
• Vicent Briva-Iglesias

• paper
• safety
• other

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