Introduces learning, potential, and retention metrics to disentangle model adaptation effects from dataset changes in adaptive AI medical devices, demonstrated on simulated population shifts.
Calibration driftinregressionandmachinelearningmodelsfor acutekidneyinjury.JournaloftheAmericanMedical InformaticsAssociation,24(6):1052–1061
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices
Introduces learning, potential, and retention metrics to disentangle model adaptation effects from dataset changes in adaptive AI medical devices, demonstrated on simulated population shifts.