Reliable Multilingual Orthopedic Decision Support from Clinical Narratives: Language-Aware Adaptation and Verification-Guided Deferral
arXiv:2605.31512v1 Announce Type: new Abstract: Multilingual orthopedic decision support remains challenging in low-resource healthcare settings, where clinical narratives contain specialized terminology, mixed scripts, incomplete evidence, label imbalance and language-dependent documentation patterns. This article presents a reliability-oriented framework for classifying free-text orthopedic notes in English, Hindi and Punjabi. We compare task-aligned multilingual transformer encoders, a task-fine-tuned DistilBERT baseline, zero-shot instruction-tuned large language models (LLMs) and a domain
The proliferation of advanced LLMs and the urgent need for scalable healthcare solutions in diverse linguistic contexts are driving this research now.
This development is important for strategic readers as it addresses a critical bottleneck in global healthcare equity and the practical application of AI in multilingual environments.
The ability to accurately classify diverse clinical narratives across multiple languages marks a significant step towards more reliable and globally applicable AI-driven decision support in medicine.
- · Global healthcare providers
- · Patients in low-resource settings
- · AI developers specializing in multilingual NLP
- · Medical AI companies
- · Healthcare systems reliant on manual documentation review
- · Ethical frameworks unprepared for multilingual AI deployment
- · Companies with single-language AI solutions
Improved diagnostic accuracy and efficiency in multilingual medical settings.
Reduced healthcare disparities and increased access to advanced medical insights in underserved regions.
The acceleration of AI adoption in other critical public services that require multilingual processing and robust decision support.
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Read at arXiv cs.CL