4 papers
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…
Coherent Hierarchical Multi-Label Learning to Defer for Medical Imaging
Joshua Strong, Pramit Saha, Emma Sun +2
Learning to Defer (L2D) enables a model to predict autonomously or defer to an expert, but prior work largely assumes flat label spaces. We study the first L2D setting with hierarc…
Clinical Note Bloat Reduction for Efficient LLM Use
Jordan L. Cahoon, Chloe Stanwyck, Asad Aali +6
Background: Clinical notes contain extensive duplicated text from templates, copy-paste, and auto-populated fields ("note bloat"), diluting clinical signal, limiting longitudinal c…