“Think of it like TurboTax. You don’t need to be a CPA to file your taxes. We want to make it possible for someone who sees fewer IPV cases—or who isn’t an expert in injury documentation—to still collect reliable evidence.”
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Anonymous donor commits additional $5.3 million to advance research
An interdisciplinary George Mason University research team is breaking new ground in using artificial intelligence to support victims of interpersonal violence. Led by Kat Scafide and Janusz Wojtusiak of the College of Public Health and David Lattanzi of the College of Engineering and Computing, the Evidence-based AI Software for Injury Detection (EAS-ID) project has successfully completed Phase 1: development of a working prototype of a mobile app designed to accurately capture and document bruises.
The tool has the potential to transform how clinicians and frontline professionals identify, record, and communicate evidence of injury, particularly in cases of interpersonal violence.
The EAS-ID app acts as a digital guidance system—similar to mobile check-deposit apps that help users capture clear, usable images. When a clinician uses the tablet-based tool, the app detects the presence of a bruise and provides real-time guidance to ensure the image meets clinical and legal standards. A rectangle tracks the injury area, much like facial recognition technology, ensuring the final image captures exactly what’s needed to document the bruise effectively.
The research team announced $4.85 million in initial funding from the same anonymous donor in March 2024.
“Initially, we were laser-focused on detection—how to identify whether a bruise was present,” said Scafide, a forensic nurse. “But Phase 1 taught us that detection is only a small part of the clinical and legal challenge. The real complexity lies in documentation.”
That documentation must be both clinically sound and legally reliable. Forensic nurses—the app’s initial target users—often assess injuries while juggling multiple tasks: shining a light, holding a camera, completing paperwork. The EAS-ID tool reduces that burden by enabling simultaneous photo capture and structured documentation.
The app’s workflow is tailored to the real-world conditions of forensic nursing. It walks users through a standardized documentation process built directly into the interface, making it possible to create accurate, consistent records that can hold up in court—even years later.
“The documentation that nurses create is reviewed by many others in the criminal justice system,” noted Lattanzi, a civil engineer. “It must be reliable—not a liability.”
To ensure the app meets forensic standards, the team conducted extensive focus groups with practicing forensic nurses. These sessions were crucial to understanding not just what needed to be documented, but how clinicians make decisions in real time under pressure. Their insights helped shape the app into a useful tool with intuitive guide rails and legal relevance.
Based on Phase 1 progress, the team is preparing to broaden its focus to include a wider range of users. “We’re exploring how to adapt the app for clinicians—and even nonclinicians—who don’t have a forensic background but still need to document injuries,” said Scafide.
Wojtusiak, a machine learning expert, added: “Think of it like TurboTax. You don’t need to be a CPA to file your taxes. We want to make it possible for someone who sees fewer IPV cases—or who isn’t an expert in injury documentation—to still collect reliable evidence.”
Behind the app, a powerful AI engine is being trained to distinguish bruises from other skin discolorations and guide users through appropriate documentation steps. To train the AI, the team is building a massive, diverse dataset. So far, they’ve partnered with Inova Health System and Adventist Healthcare Shady Grove Medical Center to collect tens of thousands of images across different skin tones, body types, and medical conditions.
“Deep learning requires scale—and the team has set a goal of collecting one million images,” said Wojtusiak.
This summer, the team will launch a crowdsourcing initiative to expand the dataset beyond clinical settings. “All of our current data was collected in labs, under controlled conditions,” explained Scafide. “But real-world data is messier. We need to understand what the photos and data will look like when collected by the public, outside of protocols.”
The app’s architecture was designed with that in mind. Using machine learning, the app evolves over time—each new image improves its performance. This allows for continual refinement, even after the app is deployed.
The project has moved at an unusually fast pace for an academic endeavor, thanks in part to the support of students and research assistants whose contributions have accelerated development while gaining invaluable experience. Continued progress has been made possible by a second round of funding from the same anonymous donor.
With a patent pending on the app’s innovative integration of image capture and health assessment data, the EAS-ID team is now preparing to scale development, deepen partnerships, and explore commercialization pathways.
“Our goal is to get this technology into the hands of people who need it—quickly,” said Scafide. “The faster we can deploy, the faster we can support the professionals working to protect and care for victims.”