RESEARCH · RESEARCH · #1103
CRC-Router: uncertainty-aware, risk-constrained routing for medical agentic AI
CRC-Router is a risk-constrained, uncertainty-aware routing module for medical prediction and agentic AI systems that combines multiple uncertainty signals with predictive scores to estimate per-finding wrong-accept risk, then applies Conformal Risk Control (CRC) to calibrate acceptance thresholds to a user-specified risk target. Instantiated on chest X-ray multi-finding triage using the NIH ChestX-ray14 dataset, the method reportedly achieves the strongest empirical risk–coverage trade-off among evaluated baselines both as a standalone router and as a plug-in for the MedRAX agent; code is public on GitHub.
KEY POINTS
- CRC-Router is a risk-constrained, uncertainty-aware routing module for medical prediction and agentic AI systems that combines multiple uncertainty signals with predictive scores to estimate per-finding wrong-accept risk, then applies Conformal Risk Control (CRC) to calibrate acceptance thresholds to a user-specified risk target.
- Instantiated on chest X-ray multi-finding triage using the NIH ChestX-ray14 dataset, the method reportedly achieves the strongest empirical risk–coverage trade-off among evaluated baselines both as a standalone router and as a plug-in for the MedRAX agent; code is public on GitHub.
- Provides a modular, model-agnostic way to control wrong-accept risk in selective automation, improving safety and deployability of medical agentic AI systems.
WHY IT MATTERS
Provides a modular, model-agnostic way to control wrong-accept risk in selective automation, improving safety and deployability of medical agentic AI systems.