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20242026
most citedToward Global Large Language Models in Medicine

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cs.CL20261 cited

Toward Global Large Language Models in Medicine

Rui Yang, Huitao Li, Weihao Xuan +47

Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large language models (LLMs) has transformed…

cs.CL2025

Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations

Rui Yang, Matthew Yu Heng Wong, Huitao Li +13

The rapid growth of medical knowledge and increasing complexity of clinical practice pose challenges. In this context, large language models (LLMs) have demonstrated value; however…

cs.CL2025

Gender Bias in Large Language Models for Healthcare: Assignment Consistency and Clinical Implications

Mingxuan Liu, Yuhe Ke, Wentao Zhu +9

The integration of large language models (LLMs) into healthcare holds promise to enhance clinical decision-making, yet their susceptibility to biases remains a critical concern. Ge…

cs.CL2025

The Evolving Landscape of Generative Large Language Models and Traditional Natural Language Processing in Medicine

Rui Yang, Huitao Li, Matthew Yu Heng Wong +12

Natural language processing (NLP) has been traditionally applied to medicine, and generative large language models (LLMs) have become prominent recently. However, the differences b…

cs.CL2024

oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness

Yu He Ke, Liyuan Jin, Kabilan Elangovan +10

Large Language Models (LLMs) show potential for medical applications but often lack specialized clinical knowledge. Retrieval Augmented Generation (RAG) allows customization with d…

cs.CL2024

Lightweight Large Language Model for Medication Enquiry: Med-Pal

Kabilan Elangovan, Jasmine Chiat Ling Ong, Liyuan Jin +9

Large Language Models (LLMs) have emerged as a potential solution to assist digital health development with patient education, commonly medication-related enquires. We trained and…