In the final interview of the AI4Purpose Hackathon series under the MEDAIGENCY initiative, Team RescueID AI shares how AI can support faster victim identification and medical coordination during disasters.
Developed by software engineering students from Yaşar University in Türkiye, the project addresses one of the most critical challenges in emergency response: identifying injured individuals quickly and ensuring they receive appropriate medical care even under damaged infrastructure and weak network conditions.
About the team
Hello everyone, first of all I’m very happy to be part of this interview and I would like to thank you for giving us the opportunity to be here. I’m Yağmur Sude Ateş, the leader of the project. My teammates are Berin Türker and Ceyda İnce. We are all senior Software Engineering students at Yaşar University.
Motivation to participate
As a team, we really enjoy participating in hackathons and challenging ourselves. We learned about this hackathon through an informational email from our university and decided to take part.
Creating a solution for natural disasters, something our country unfortunately faces often and that causes major destruction, became our biggest source of motivation.
AI Solution: RescueID AI
Our team developed RescueID AI, a system that helps identify disaster victims and coordinate their medical treatment quickly.
In large disasters, many injured people cannot be identified and medical teams often don’t know their health history. Our system allows rescue teams to scan a fingerprint using only a tablet camera, identify the person, and access critical health information such as blood type or chronic diseases.
AI helps with fingerprint recognition and intelligent hospital recommendations by analyzing both patient data and hospital capacity. The goal is to send each patient to the most suitable hospital as quickly as possible.
What makes our solution innovative is that it works even with very weak internet connections by sending small encrypted hash data instead of large fingerprint images.
Future development
Our next step is to develop a fully functional prototype and improve the system’s technical components. This includes optimizing the AI-based fingerprint recognition model, improving low-bandwidth data transmission, and strengthening the integration with health and hospital information systems.
We also plan to test the system in simulated disaster environments to evaluate its performance under weak connectivity conditions.
In the long term, RescueID AI could be integrated into national emergency response infrastructures, supporting rescue teams, ambulances, and hospitals with real-time identification and patient routing.
Collaboration with institutions, healthcare authorities, and projects like MEDAIGENCY could help us access real datasets, improve interoperability, and move the solution closer to real-world deployment.
Mediterranean perspective
Participating in a Mediterranean-level competition was a very meaningful experience for us.
Many countries in the Mediterranean region face similar risks such as earthquakes, wildfires, and large-scale emergencies, so it was inspiring to work alongside teams addressing comparable challenges.
The international environment encouraged us to think about scalability and adaptability, and how our solution could potentially support disaster response beyond a single country.
At the same time, interacting with teams, mentors, and jury members from different backgrounds helped us gain new perspectives on how AI can be used for humanitarian impact and cross-border collaboration.
We would like to warmly thank Mr. Ayman Rahmeh, MEDAIGENCY Communications Manager at the American University of Beirut, for facilitating this series of interviews.
Next Med
