Researchers Develop AI Model to Better Predict Which Drugs May Cause Birth Defects

Data scientists at the Icahn School of Medicine at Mount Sinai in New York and colleagues have created an artificial intelligence model that may more accurately predict which existing medicines, not currently classified as harmful, may in fact lead to congenital disabilities. The model, or “knowledge graph,” described in the July 17 issue of the Nature journal Communications Medicine, also has the potential to predict the involvement of pre-clinical compounds that may harm the developing fetus. The study is the first known of its kind to use knowledge graphs to integrate various data types to investigate the causes of congenital disabilities.

AI-guided screening uses ECG data to detect a hidden risk factor for stroke

An AI-guided targeted screening strategy is effective in detecting new cases of atrial fibrillation that would not have come to attention in routine clinical care.
This strategy could reduce the number of undiagnosed cases of atrial fibrillation, and prevent stroke and death in millions of patients across the globe.