Researchers described an explainable AI framework designed to address intentional injury mortality—covering suicide and homicide—using a publication highlighted in Scientific Reports. The approach is described as explainable, aiming to translate model outputs into actionable insights rather than black-box risk scoring. Public-health teams and researchers could use the framework to better understand drivers of injury mortality in the Americas, supporting more targeted prevention and response strategies. The emphasis on explainability also suggests a focus on accountability and interpretability for downstream use.