Artificial intelligence-based strategies to identify patient populations and advance analysis in age-related macular degeneration clinical trials. (July 2022) - Yaghy, Antonio; Lee, Aaron Y.; Keane, Pearse A.; Keenan, Tiarnan D.L.; Mendonca, Luisa S.M.; Lee, Cecilia S.; Cairns, Anne Marie; Carroll, Joseph; Chen, Hao; Clark, Julie; Cukras, Catherine A.; de Sisternes, Luis; et al.
Highlights: Machine learning models predict risk for adverse events and likelihood of dropout. Machine learning models predict disease progression risk and response to treatment. Predictive models can be of great use in all stages of clinical trials. Training data sets have to be diverse to maximize generalizability. It is key to ensure patient safety and ethical use of the models.