Toward Global Large Language Models in Medicine
By: Rui Yang , Huitao Li , Weihao Xuan and more
Potential Business Impact:
Helps doctors understand sick people in any language.
Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large language models (LLMs) has transformed the landscape of medicine and holds promise for improving health care quality and expanding access to medical information globally. However, existing LLMs are primarily trained on high-resource languages, limiting their applicability in global medical scenarios. To address this gap, we constructed GlobMed, a large multilingual medical dataset, containing over 500,000 entries spanning 12 languages, including four low-resource languages. Building on this, we established GlobMed-Bench, which systematically assesses 56 state-of-the-art proprietary and open-weight LLMs across multiple multilingual medical tasks, revealing significant performance disparities across languages, particularly for low-resource languages. Additionally, we introduced GlobMed-LLMs, a suite of multilingual medical LLMs trained on GlobMed, with parameters ranging from 1.7B to 8B. GlobMed-LLMs achieved an average performance improvement of over 40% relative to baseline models, with a more than threefold increase in performance on low-resource languages. Together, these resources provide an important foundation for advancing the equitable development and application of LLMs globally, enabling broader language communities to benefit from technological advances.
Similar Papers
Multimodal Large Language Models for Medicine: A Comprehensive Survey
Machine Learning (CS)
Helps doctors understand sickness using pictures and words.
Large Language Models in Healthcare
Computers and Society
Helps doctors use smart computers for better patient care.
Advances in Large Language Models for Medicine
Artificial Intelligence
Helps doctors understand patient health better.