AI in Pre-hospital & Critical Care: Revolutionizing Emergency Medicine

Published Date : 23/06/2025 

Explore the groundbreaking role of Artificial Intelligence (AI) in pre-hospital and critical care settings, including AI-based triage tools, early warning systems, and predictive analytics. Discover how these innovations are shaping the future of emergency medicine. 

The field of emergency medicine is witnessing a revolutionary transformation thanks to the integration of Artificial Intelligence (AI). This special collection, curated by the Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, delves into the current and future applications of AI in pre-hospital and critical care settings.

AI is rapidly becoming an indispensable tool in emergency medicine, offering a wide range of applications that can significantly improve patient outcomes. From AI-based triage tools to early warning systems, the potential is vast. AI can help in the early detection of life-threatening conditions, enabling timely interventions that can save lives.

One of the key areas of focus is the development of AI-based triage tools. These systems can quickly assess the severity of a patient's condition and prioritize care, ensuring that the most critical cases receive immediate attention. For instance, AI algorithms can analyze vital signs, medical history, and real-time data from wearable devices to provide accurate triage decisions.

Another critical application is the use of early warning systems. These systems can monitor patients continuously and alert healthcare providers to potential deteriorations in their condition. By detecting subtle changes that might be missed by human observation, AI can help prevent complications and improve patient outcomes.

Predictive analytics is another area where AI is making a significant impact. By analyzing large datasets, AI can identify patterns and predict outcomes, helping healthcare providers make more informed decisions. For example, AI can predict the likelihood of cardiac arrest or other critical events, allowing for proactive interventions.

AI is also being integrated into decision support systems for cardiac arrest and trauma. These systems can provide real-time guidance to healthcare providers, helping them make rapid and accurate decisions in high-pressure situations. For instance, AI can suggest the most appropriate treatment protocols based on the patient's specific condition and medical history.

Wearable and ambient sensing technologies are another exciting area of development. These devices can monitor patients' vital signs and other health parameters continuously, providing valuable data that can be used to inform clinical decisions. For example, wearable devices can alert healthcare providers to changes in a patient's heart rate or blood pressure, enabling timely interventions.

The integration of AI with dispatch and navigation systems is also a growing trend. AI can help optimize the dispatch of emergency services, ensuring that the most appropriate resources are sent to the scene as quickly as possible. Additionally, AI can provide real-time navigation guidance to emergency responders, helping them reach the scene more efficiently.

However, the integration of AI in emergency medicine also raises important ethical and regulatory considerations. Ensuring the privacy and security of patient data is paramount, and there is a need for clear guidelines and regulations to govern the use of AI in healthcare. Additionally, the validation of AI tools in high-stakes environments is crucial to ensure their reliability and effectiveness.

We welcome original research, scoping reviews, methodological papers, and implementation case studies from both academic and clinical perspectives. The collection seeks to inform clinicians, researchers, and policymakers about the current landscape and future potential of AI-driven innovations in emergency medicine.

If you are interested in contributing to this collection, please submit your articles via our submission system, Snapp. Select the appropriate Collection title “Artificial Intelligence in Pre-hospital and Critical Care: Innovations, Applications, and Future Directions” under the “Details” tab during the submission stage. Articles will undergo the journal’s standard peer-review process and are subject to all the journal’s standard policies.

The Editors have no competing interests with the submissions which they handle through the peer-review process. The peer-review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests. 

Frequently Asked Questions (FAQS): 

Q: What are AI-based triage tools used for in emergency medicine?

A: AI-based triage tools are used to quickly assess the severity of a patient's condition and prioritize care, ensuring that the most critical cases receive immediate attention.

Q: How does AI help in early warning systems?

A: AI can monitor patients continuously and alert healthcare providers to potential deteriorations in their condition, helping to prevent complications and improve patient outcomes.

Q: What is the role of predictive analytics in emergency medicine?

A: Predictive analytics uses AI to analyze large datasets and predict outcomes, helping healthcare providers make more informed decisions and take proactive interventions.

Q: How are wearable and ambient sensing technologies used in emergency care?

A: Wearable and ambient sensing technologies monitor patients' vital signs and other health parameters continuously, providing valuable data to inform clinical decisions and alert healthcare providers to changes in a patient's condition.

Q: What ethical and regulatory considerations are important in the use of AI in emergency medicine?

A: Ethical and regulatory considerations in the use of AI in emergency medicine include ensuring the privacy and security of patient data, clear guidelines and regulations, and the validation of AI tools in high-stakes environments to ensure their reliability and effectiveness. 

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