Abstract:

In mass casualty events and resource-constrained scenarios, limited responder capacity leads to preventable deaths. Time is of the essence particularly in severe trauma: the sooner individuals receive care, the higher their chances of survival. Yet a single responder can only manage a few patients simultaneously, leaving others unattended. This thesis addresses this capacity constraint by developing intelligent robotic systems that serve as medical force multipliers, enabling effective emergency response when casualties outnumber available help.

This work presents two embodied Artificial Intelligence (AI) platforms for emergency medical response in unstructured field environments. The first performs multipatient automated assessment, which uses contactless multimodal sensors to identify qualitative (e.g., wounds, amputations, hemorrhage, respiratory distress) and quantitative vital signs (e.g., heart rate) to rapidly assess and prioritize the most critically injured. The second automates fluid resuscitation, targeting hemorrhage, the leading cause of preventable death in trauma. The pipeline comprises multiple stages: vessel localization and segmentation, visualization and uncertainty quantification for safe decision-making, bifurcation detection for anatomically-informed needle placement, and real-time needle tracking.

To address the scarcity of training data in emergency medicine robotics, this research embeds expert clinical knowledge and applies weak supervision techniques, enabling robust performance with limited labeled examples. All algorithms execute in real time on resource-constrained platforms, with key components designed to adapt to changing environmental conditions.

This thesis contributes to autonomous medical systems and offers new methodologies for developing AI solutions for life-critical applications in unstructured environments where traditional data-driven approaches may fail. By augmenting human responders, we show how robotic systems can expand treatment capacity when it matters most, potentially saving lives that would otherwise be lost.

Thesis Committee:

Artur Dubrawski, Chair

Jean Oh

Fernando de la Torre Frade

Daniel McDuff, Google

Laura Brattain, University of Central Florida

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