1 citations · 1 across the 8 of their papers we have counts for
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Learning to Defer with Guidance on Real World Medical Data
Emma Sun, Joshua Strong, Alison Noble
Medical image interpretation is high-volume and time-consuming, and while AI interpretation can reduce workload, fully autonomous deployment carries potential safety concerns and l…
RACER: Role-Aligned Competence Estimation for Human-AI Routing
Joshua Strong, Emma Sun, Alexander Capstick +3
Learning to defer asks a predictive system when to act autonomously and when to defer to a human expert. Population-adaptive deferral extends this problem to unseen experts using a…
Picking the Right Specialist: Attentive Neural Process-based Selection of Task-Specialized Models as Tools for Agentic Healthcare Systems
Pramit Saha, Joshua Strong, Mohammad Alsharid +2
Task-specialized models form the backbone of agentic healthcare systems, enabling the agents to answer clinical queries across tasks such as disease diagnosis, localization, and re…
FedAgentBench: Towards Automating Real-world Federated Medical Image Analysis with Server-Client LLM Agents
Pramit Saha, Joshua Strong, Divyanshu Mishra +2
Federated learning (FL) allows collaborative model training across healthcare sites without sharing sensitive patient data. However, real-world FL deployment is often hindered by c…
Identity-Free Deferral For Unseen Experts
Joshua Strong, Pramit Saha, Yasin Ibrahim +2
Learning to Defer (L2D) improves AI reliability in decision-critical environments by training AI to either make its own prediction or defer the decision to a human expert. A key ch…