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Artificial Intelligence in Disaster Triage: Enhancing Emergency Nursing Practice Through Decision Support.

Mona Gamal Mohamed, Jamla Rizek

Journal of Emergency Nursing

|July 1, 2026

Summary

Artificial intelligence (AI) can enhance emergency nursing during mass casualty incidents by improving triage accuracy and resource allocation. However, careful implementation with nurse involvement is crucial to avoid errors and maintain clinical judgment.

Keywords:

Artificial intelligence Clinical safety Decision-support systems Disaster triage Emergency nursing Mass casualty incidents

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Area of Science:

Emergency Medicine
Disaster Response
Artificial Intelligence in Healthcare

Background:

Mass casualty incidents demand rapid, high-stress decision-making from emergency nurses.
Existing triage systems, while foundational, are susceptible to human error under cognitive load.
Increasingly complex disasters necessitate innovative tools for effective frontline response.

Purpose of the Study:

To explore the potential of AI-powered triage and decision-support systems in disaster response.
To examine the benefits and challenges of integrating AI into emergency nursing practice during mass casualty incidents.
To identify critical factors for the successful implementation of AI in disaster triage.

Main Methods:

Review of emerging AI applications in disaster response, including tele-triage and injury prediction.
Analysis of challenges associated with AI integration, such as data bias and transparency.
Discussion of essential elements for effective AI implementation in emergency nursing.

Main Results:

AI systems leveraging machine learning and real-time data show promise in enhancing triage accuracy, resource optimization, and situational awareness.
Real-world examples demonstrate AI's utility in rural tele-triage and post-earthquake injury prediction.
Implementation challenges include data bias, lack of transparency, and potential erosion of clinical judgment.

Conclusions:

AI offers significant potential to augment emergency nursing during disasters, enabling more accurate and coordinated care.
Active nurse involvement in AI system co-design and robust training are essential for safe and effective integration.
Ethical considerations and clear protocols are paramount to ensure AI supports, rather than hinders, clinical practice.