Northwestern Medicine custom GenAI system built in-house helps radiologists deliver faster reports and flag life-threatening conditions.
Northwestern Medicine has deployed a fully embedded GenAI tool across its radiology network, increasing report creation speed by up to 40% without compromising accuracy. The system, trained on internal clinical data, was developed in-house to support radiologists in high-pressure environments. It generates patient-specific, 95% complete draft reports instantly for all types of X-rays.
Unlike traditional AI tools that detect specific conditions, this holistic GenAI model interprets the entire image and creates a structured summary. Radiologists review and finalize the drafts, significantly cutting down report times. Over 24,000 radiographs were studied, showing a 15.5% average efficiency gain, with some radiologists doubling productivity.
The tool also flags critical conditions—like collapsed lungs—before a human sees the image. An automated layer monitors the GenAI-generated reports and alerts teams to urgent findings. This enables faster triage in busy emergency departments and could help detect early-stage diseases such as lung cancer. By improving speed and prioritization, GenAI reduces the chance of delayed diagnoses.
Unlike many off-the-shelf tools, Northwestern’s model is custom-built for radiology, runs on lighter infrastructure, and avoids third-party dependencies like ChatGPT. This approach allows health systems to build secure, targeted GenAI without relying on tech giants. As radiologist shortages worsen, the model provides a scalable solution to help clear imaging backlogs and improve patient outcomes—without replacing clinicians.