Researchers developed 'explainable' AI to help diagnose and treat at-risk children

A pair of researchers from the Oak Ridge Laboratory have developed an "explainable" AI system designed to aid medical professionals in the diagnosis and treatment of children and adults who've experienced childhood adversity. While this is a decidedly narrow use-case, the nuts and bolts behind this AI have particularly interesting implications for the machine learning field as a whole. Plus, it represents the first real data-driven solution to the outstanding problem of empowering general medical practitioners with expert-level domain diagnostic skills – an impressive feat in itself. Let's start with some background. Adverse childhood experiences (ACEs) are a well-studied form of medically relevant environmental factors whose effect on people, especially those in minority communities, throughout the entirety of their lives has been thoroughly researched. While the symptoms and outcomes are often difficult to diagnose and predict, the most common interventions are usually easy to employ.