AI-Driven Optimization of Emergency Medical Services

Timely and informed response in emergency medical services (EMS) can significantly increase survival rates. Conventional ambulance services encounter delays in recognizing patients, accessing medical records, and coordinating communication between on-site teams and medical facilities. This study introduces an AI-driven ambulance system that incorporates real-time facial recognition, GPS tracking, and retrieval of patient information through biometric authentication. A compact machine learning model (MobileFaceNet) is implemented on Android using TensorFlow Lite, allowing for instantaneous facial recognition. The mobile application captures the patient's identity, connects with a backend to retrieve medical history, and synchronizes GPS information for adaptive routing and notifications to hospitals. The findings show a decrease in the time taken for patient identification, enhanced readiness of hospitals, and improved responsiveness in emergencies.