19 citations · 41 across the 11 of their papers we have counts for
11 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…
When Fine-Tuning Changes the Evidence: Architecture-Dependent Semantic Drift in Chest X-Ray Explanations
Kabilan Elangovan, Daniel Ting
Transfer learning followed by fine-tuning is widely adopted in medical image classification due to consistent gains in diagnostic performance. However, in multi-class settings with…
Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification
Kabilan Elangovan, Daniel Ting
Class Activation Mapping (CAM) methods are widely used to generate visual explanations for deep learning classifiers in medical imaging. However, existing evaluation frameworks ass…
Clinical Validation of Medical-based Large Language Model Chatbots on Ophthalmic Patient Queries with LLM-based Evaluation
Ting Fang Tan, Kabilan Elangovan, Andreas Pollreisz +13
Domain specific large language models are increasingly used to support patient education, triage, and clinical decision making in ophthalmology, making rigorous evaluation essentia…
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…
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…
A Proposed S.C.O.R.E. Evaluation Framework for Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability
Ting Fang Tan, Kabilan Elangovan, Jasmine Ong +10
A comprehensive qualitative evaluation framework for large language models (LLM) in healthcare that expands beyond traditional accuracy and quantitative metrics needed. We propose…
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…