Doctors often must make critical decisions in minutes, relying on incomplete information. While electronic health records contain vast amounts of patient data, much of it remains difficult to interpret quickly—especially for patients with rare diseases or unusual symptoms. Now, researchers at the Icahn School of Medicine at Mount Sinai and collaborators have developed an artificial intelligence system, called InfEHR, that links unconnected medical events over time, creating a diagnostic web that reveals hidden patterns. Published in the September 26 online issue of Nature Communications, the study shows that Inference on Electronic Health Records (InfEHR) transforms millions of scattered data points into actionable, patient-specific diagnostic insights.