19 citations · 55 across the 14 of their papers we have counts for
15 papers
Toward Global Large Language Models in Medicine
Rui Yang, Huitao Li, Weihao Xuan +48
Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large language models (LLMs) has transformed…
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…
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…
EVLF-FM: Explainable Vision Language Foundation Model for Medicine
Yang Bai, Haoran Cheng, Yang Zhou +40
Despite the promise of foundation models in medical AI, current systems remain limited - they are modality-specific and lack transparent reasoning processes, hindering clinical ado…
Multimodal, Multi-Disease Medical Imaging Foundation Model (MerMED-FM)
Yang Zhou, Chrystie Wan Ning Quek, Jun Zhou +22
Current artificial intelligence models for medical imaging are predominantly single modality and single disease. Attempts to create multimodal and multi-disease models have resulte…
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…
Real-world Deployment and Evaluation of PErioperative AI CHatbot (PEACH) -- a Large Language Model Chatbot for Perioperative Medicine
Yu He Ke, Liyuan Jin, Kabilan Elangovan +10
Large Language Models (LLMs) are emerging as powerful tools in healthcare, particularly for complex, domain-specific tasks. This study describes the development and evaluation of t…
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis
Siqi Li, Xin Li, Kunyu Yu +12
Clinical and biomedical research in low-resource settings often faces significant challenges due to the need for high-quality data with sufficient sample sizes to construct effecti…
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…
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
Mingxuan Liu, Yilin Ning, Yuhe Ke +5
The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness. We propose an interpretable framework - Fa…
Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties
Jasmine Chiat Ling Ong, Liyuan Jin, Kabilan Elangovan +13
Importance: We introduce a novel Retrieval Augmented Generation (RAG)-Large Language Model (LLM) framework as a Clinical Decision Support Systems (CDSS) to support safe medication…
Fine-tuning Large Language Model (LLM) Artificial Intelligence Chatbots in Ophthalmology and LLM-based evaluation using GPT-4
Ting Fang Tan, Kabilan Elangovan, Liyuan Jin +9
Purpose: To assess the alignment of GPT-4-based evaluation to human clinician experts, for the evaluation of responses to ophthalmology-related patient queries generated by fine-tu…
Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
YuHe Ke, Liyuan Jin, Kabilan Elangovan +7
Purpose: Large Language Models (LLMs) hold significant promise for medical applications. Retrieval Augmented Generation (RAG) emerges as a promising approach for customizing domain…
Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias
Yu He Ke, Rui Yang, Sui An Lie +4
Background: Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formida…
Integrating UMLS Knowledge into Large Language Models for Medical Question Answering
Rui Yang, Edison Marrese-Taylor, Yuhe Ke +3
Large language models (LLMs) have demonstrated powerful text generation capabilities, bringing unprecedented innovation to the healthcare field. While LLMs hold immense promise for…