19 citations · 34 across the 4 of their papers we have counts for
4 papers
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…
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…
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…