An Artificial Intelligence Copilot for First Response Medicine

Area of Science:

• Emergency Medicine
• Artificial Intelligence
• Augmented Reality

Background:

• First responders face challenging environments with limited resources and communication.
• Procedural accuracy is critical for patient outcomes in emergency medicine.
• Existing AI systems are often designed for controlled hospital settings, not field use.

Purpose of the Study:

• To develop and validate an AI Copilot system for first responders in emergency medicine.
• To provide real-time procedural guidance, decision support, and skill evaluation.
• To enhance medical care in austere, resource-limited field environments.

Main Methods:

• A feasibility and technical validation study using an augmented reality (AR) headset and a GPU-equipped microprocessor.
• An edge-based AI system with machine learning models for action recognition, anticipation, visual question answering, hand tracking, and object detection.
• Performance benchmarking against expert-labeled ground truth using the Trauma THOMPSON dataset.

Main Results:

• The MViTv2 model achieved high accuracy for action recognition (89.75%) and anticipation (87.02%).
• The BLIP model demonstrated strong Visual Question Answering accuracy (88.64%) for clinical decision support.
• The system achieved 100% accuracy in differentiating expert and novice skill levels based on hand kinematics.

Conclusions:

• The AI Copilot is optimized for field care (Role 1-2), emphasizing autonomy and resilience.
• The AR-based system offers real-time mentorship, reducing cognitive load and improving procedural accuracy.
• This technology bridges the gap between expert and frontline medical care in high-stakes scenarios.