7 citations · 20 across the 15 of their papers we have counts for
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cs.AI2026
Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices
Alexis Burgon, Berkman Sahiner, Nicholas A Petrick +2
This work addresses challenges in evaluating adaptive artificial intelligence (AI) models for medical devices, where iterative updates to both models and evaluation datasets compli…
cs.AI2024
Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control
Ghada Zamzmi, Kesavan Venkatesh, Brandon Nelson +4
Background: Machine learning (ML) methods often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices in clinical sett…