68 citations · 117 across the 11 of their papers we have counts for
11 papers
RuleEdit: Failure-Guided Human-AI Model Editing with Prospective Impact Preview
Min Hun Lee, Justin Yu Feng Teo
Despite the promise of AI to assist complex decisions, practitioners still lack ways to detect likely failures and inspect the consequences of model edits before committing them. W…
From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction
Pujun Feng, Xiaoyu Guo, Seyed Ehsan Saffari +10
Clinical decision-making is a feedback system where risk estimates influence treatment, which in turn changes disease trajectories, and both shape clinicians' measurement practices…
From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Making
Min Hun Lee
Artificial intelligence (AI) systems are deployed as collaborators in human decision-making. Yet, evaluation practices focus primarily on model accuracy rather than whether human-A…
MedSAD-CLIP: Supervised CLIP with Token-Patch Cross-Attention for Medical Anomaly Detection and Segmentation
Thuy Truong Tran, Minh Kha Do, Phuc Nguyen Duy +1
Medical anomaly detection (MAD) and segmentation play a critical role in assisting clinical diagnosis by identifying abnormal regions in medical images and localizing pathological…
Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Making
Min Hun Lee, Martyn Zhe Yu Tok
Despite the growing promise of artificial intelligence (AI) in supporting decision-making across domains, fostering appropriate human reliance on AI remains a critical challenge. I…
Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Making
Min Hun Lee, Silvana Xin Yi Choo, Shamala D/O Thilarajah
With advanced AI/ML, there has been growing research on explainable AI (XAI) and studies on how humans interact with AI and XAI for effective human-AI collaborative decision-making…
Towards Gradient-based Time-Series Explanations through a SpatioTemporal Attention Network
Min Hun Lee
In this paper, we explore the feasibility of using a transformer-based, spatiotemporal attention network (STAN) for gradient-based time-series explanations. First, we trained the S…
Exploring a Multimodal Fusion-based Deep Learning Network for Detecting Facial Palsy
Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim
Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessment by clinicians. In this pape…
Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making
Min Hun Lee, Chong Jun Chew
Artificial intelligence (AI) is increasingly being considered to assist human decision-making in high-stake domains (e.g. health). However, researchers have discussed an issue that…
Design, Development, and Evaluation of an Interactive Personalized Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic +2
Socially assistive robots are increasingly being explored to improve the engagement of older adults and people with disability in health and well-being-related exercises. However,…
Exploring a Gradient-based Explainable AI Technique for Time-Series Data: A Case Study of Assessing Stroke Rehabilitation Exercises
Min Hun Lee, Yi Jing Choy
Explainable artificial intelligence (AI) techniques are increasingly being explored to provide insights into why AI and machine learning (ML) models provide a certain outcome in va…