13 citations · 20 across the 10 of their papers we have counts for
4 papers · 1 filter
Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features
Miguel A. Lago, Ghada Zamzmi, Brandon Eich +1
Explainability features are intended to provide insight into the internal mechanisms of an AI device, but there is a lack of evaluation techniques for assessing the quality of prov…
Scorecards for Synthetic Medical Data Evaluation and Reporting
Ghada Zamzmi, Adarsh Subbaswamy, Elena Sizikova +3
Although interest in synthetic medical data (SMD) for training and testing AI methods is growing, the absence of a standardized framework to evaluate its quality and applicability…
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…
The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts
A Badano, M Lago, E Sizikova +4
Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trial…