7 citations · 20 across the 15 of their papers we have counts for
5 papers · 1 filter
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
Jean Feng, Adarsh Subbaswamy, Alexej Gossmann +7
After a machine learning (ML)-based system is deployed, monitoring its performance is important to ensure the safety and effectiveness of the algorithm over time. When an ML algori…
Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses
Elena Sizikova, Niloufar Saharkhiz, Diksha Sharma +4
To generate evidence regarding the safety and efficacy of artificial intelligence (AI) enabled medical devices, AI models need to be evaluated on a diverse population of patient ca…
Is this model reliable for everyone? Testing for strong calibration
Jean Feng, Alexej Gossmann, Romain Pirracchio +3
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup. Such models are reliable across heterogeneous…
Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center Open Data Commons
Heather M. Whitney, Natalie Baughan, Kyle J. Myers +11
Purpose: The Medical Imaging and Data Resource Center (MIDRC) open data commons was launched to accelerate the development of artificial intelligence (AI) algorithms to help addres…
Evaluation of wait time saving effectiveness of triage algorithms
Yee Lam Elim Thompson, Gary M Levine, Weijie Chen +7
In the past decade, Artificial Intelligence (AI) algorithms have made promising impacts to transform healthcare in all aspects. One application is to triage patients' radiological…