Edge AI Coordination Systems for Mass Casualty Response

Abstract

Mass casualty incidents (MCIs) present extraordinary challenges to healthcare systems, necessitating rapid situational assessment, triage, and coordinated response. The emergence of edge Artificial Intelligence (AI) technologies offers transformative potential for real-time decision-making and resource optimization in such high-stakes environments. This review examines the architecture, clinical implications, and practical deployment of edge AI coordination systems in MCI scenarios, synthesizing evidence from recent literature and discussing the impact on triage accuracy, workflow efficiency, and patient outcomes. Key mechanisms, risk considerations, and guideline-based recommendations are highlighted to inform clinicians and healthcare administrators.

Introduction

Mass casualty events, including natural disasters, industrial accidents, and acts of terrorism, can rapidly overwhelm conventional healthcare infrastructure. Effective response requires timely information, dynamic coordination, and robust communication under conditions of uncertainty and resource scarcity. Traditional systems often struggle with data latency and bottlenecks, resulting in suboptimal triage and delayed interventions. Edge AI coordination systems, which deploy intelligent algorithms directly on local devices at the incident site, promise to mitigate these challenges by providing autonomous, context-aware analytics and supporting distributed decision-making. This article explores the scientific foundations, clinical relevance, and implementation strategies for edge AI in mass casualty response, focusing on mechanisms that enhance operational effectiveness and patient care.

Epidemiology / Disease Burden

Global data reflects a persistent rise in the frequency and scale of MCIs, driven by climate change, geopolitical instability, and urbanization. According to the World Health Organization, disasters annually affect over 160 million individuals and cause hundreds of thousands of deaths. MCIs commonly result in a surge of trauma, burns, crush injuries, and toxic exposures, often overwhelming local emergency services. The unpredictable distribution of casualties, combined with limited resources and infrastructure collapse, underscores the urgent need for innovative coordination technologies. Edge AI systems have been deployed in drill exercises and real-world incidents, demonstrating improved throughput and triage efficiency compared to manual methods.

Pathophysiology

Understanding the pathophysiological spectrum in MCIs is critical for optimizing edge AI algorithms. The primary injuries encountered hemorrhagic shock, airway compromise, polytrauma, and burns require rapid identification and prioritization. Edge AI systems analyze multimodal data, including vital signs, imaging, and environmental sensors, to flag critical pathologies and stratify risk. These algorithms model the physiological trajectories of casualties, predicting decompensation and enabling early intervention. For example, AI-driven pulse oximetry and capnography can autonomously alert responders to impending hypoxia or respiratory failure, thus supporting life-saving action even before explicit clinical assessment.

Risk Factors

Several risk factors influence the effectiveness of mass casualty response and the deployment of edge AI systems. These include the complexity of the incident environment, the heterogeneity of injuries, the presence of hazardous materials, and the baseline capabilities of the local healthcare system. Comorbidities such as cardiovascular disease, diabetes, and advanced age increase morbidity and mortality among casualties. Edge AI systems can incorporate patient medical history, either from preloaded databases or real-time linkage to health records, to adjust triage algorithms for individual risk profiles. However, reliance on technology introduces new risks, including potential device failure, cybersecurity threats, and ethical considerations regarding algorithmic bias.

Clinical Features

The clinical presentation in MCIs is often heterogeneous, ranging from minor injuries to life-threatening trauma. Edge AI coordination systems facilitate the rapid identification of critical features such as airway obstruction, active bleeding, altered consciousness, and shock states. Using wearable sensors, portable ultrasound, and point-of-care diagnostics, edge AI can autonomously assess and categorize casualties according to standardized triage protocols like START (Simple Triage and Rapid Treatment) or SALT (Sort, Assess, Lifesaving interventions, Treatment/Transport). Real-time feedback supports frontline responders in making evidence-based decisions, reducing cognitive overload and minimizing errors during high-pressure scenarios.

Diagnosis

Diagnostic accuracy is paramount in MCIs. Edge AI leverages continuous data streams from biosensors, imaging devices, and environmental inputs to synthesize a comprehensive patient assessment. Advanced machine learning models recognize injury patterns, detect occult shock, and forecast deterioration. For example, computer vision algorithms can interpret digital images of wounds or burns, while predictive analytics integrate vital sign trends to stratify hemorrhagic risk. The decentralization of diagnostic processing to the point of care enables rapid, context-sensitive decision-making, even in environments with limited connectivity.

Treatment & Management

Management strategies in MCIs hinge on efficient triage, resource allocation, and timely intervention. Edge AI coordination systems provide dynamic mapping of casualties, available personnel, ambulances, and critical supplies, optimizing dispatch and transport logistics. Automated recommendations guide responders in the selection of airway management techniques, fluid resuscitation, and hemorrhage control, tailored to each patient's physiological status. Integration with electronic health records and telemedicine platforms facilitates remote expert consultation and ongoing clinical oversight. Edge AI also supports after-action reviews by generating detailed logs and performance analytics, driving continuous improvement in MCI preparedness and response.

Recent Advances / Emerging Therapies

Recent advances have expanded the scope of edge AI in MCI settings. Next-generation wearables offer high-fidelity physiological monitoring, while federated learning enables cross-institutional algorithm training without compromising data privacy. Mobile edge computing platforms now support low-latency, high-throughput analytics in field conditions, and natural language processing assists with real-time documentation and communication. Emerging therapies include AI-assisted field blood transfusion, drone-supported supply delivery, and robotics-enabled casualty extraction. These innovations are being validated through simulation, pilot deployments, and integration into national disaster response frameworks.

Guideline Recommendations

International organizations, including the World Health Organization and the American College of Emergency Physicians, increasingly recognize the role of digital health and AI in disaster preparedness. Current guidelines advocate for the integration of edge AI systems into MCI protocols, emphasizing interoperability, data security, and clinician oversight. Best practices include continuous algorithm validation, transparent reporting of decision logic, and training for frontline staff in the deployment and troubleshooting of AI-enabled devices. Ethical frameworks must be established to safeguard patient autonomy and ensure equitable access to advanced technologies in mass casualty settings.

Conclusion

Edge AI coordination systems represent a paradigm shift in mass casualty response, offering unprecedented capabilities for real-time analytics, triage precision, and resource optimization. While challenges remain in terms of infrastructure, training, and ethical governance, the accumulating evidence supports the integration of edge AI into disaster medicine protocols. Ongoing research and cross-sector collaboration will be essential to fully realize the clinical and operational benefits of these technologies, ultimately enhancing outcomes for patients affected by large-scale emergencies.