19 citations · 36 across the 5 of their papers we have counts for
5 papers
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…
Towards clinical AI fairness: A translational perspective
Mingxuan Liu, Yilin Ning, Salinelat Teixayavong +12
Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the issue of fairness remains a concern in high-stakes fields such as healthcare. Despi…
Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting
Jonathan Hehir, Daniel Ting, Graham Cormode
Data sketching is a critical tool for distinct counting, enabling multisets to be represented by compact summaries that admit fast cardinality estimates. Because sketches may be me…