19 citations · 36 across the 6 of their papers we have counts for
6 papers
Position: Explanation Stability Is a Property of the Model Method Pair, Not the Model
Kabilan Elangovan, Daniel Ting
This position paper argues that claims about explanation stability are scientifically invalid without cross method validation. Just as statistical significance requires the test st…
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…
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…
Perception-Aligned AI Outputs: End-to-End Visual Prediction for Uncertainty Communication in Clinical Decision-Making
Mohammad Eslami, Solale Tabarestani, Saber Kazeminasab +9
Explainable Artificial Intelligence (XAI) is essential for trustworthy AI in healthcare, yet many existing methods rely on technical explanations that are difficult for clinicians…