Artificial Intelligence in Emergency Medicine

Artificial Intelligence in Emergency Medicine (AIEM) is a gold open access, peer-reviewed journal dedicated to advancing the science, practice, and impact of artificial intelligence (AI) across the entire spectrum of emergency medicine. The journal welcomes high-quality, original research, reviews, technical innovations, perspectives, and case reports from all regions and disciplines.

AIEM provides a comprehensive forum for clinicians, researchers, informaticians, engineers, policymakers, and industry leaders to share knowledge, foster collaboration, and accelerate the responsible adoption of AI in emergency medicine and acute care settings globally.

It is important to note that any submission to AIEM will only be considered if the work is relevant to both emergency medicine and AI, which we define as follows.

Emergency medicine: Emergency Medicine is a primary specialty focused on the prevention, diagnosis, and management of urgent and emergency conditions affecting patients of all ages with diverse and undifferentiated disorders. It involves timely triage, resuscitation, telemedicine, and coordinated care both in and out of hospital settings until patient stabilization or transfer.

Artificial intelligence: The development and application of computational systems that emulate aspects of human cognition and decision-making, underpinned by heuristics, statistical modelling, traditional machine learning or deep learning approaches.

The journal’s scope includes, but is not limited to, the areas listed below.

1. Clinical Applications and Patient Care

• AI-powered diagnostics, triage, and risk stratification for all emergency conditions, including trauma, cardiovascular events, neurological emergencies, sepsis, and more.

• Real-time clinical decision support systems, including imaging, point-of-care, and wearable sensor applications.

• AI-enabled remote monitoring, telehealth, and prehospital care solutions.

• Innovations in pediatric, geriatric, and special populations’ emergency care using AI.

2. Operations, Systems, and Resource Management

• AI-driven workflow optimization, patient flow, and throughput in emergency departments and prehospital settings.

• Predictive analytics for surge management, disaster response, mass casualty incidents, and public health emergencies.

• Integration of AI with electronic health records, hospital information systems, and regional/national emergency care networks.

3. Data Science, Informatics, and Computational Methods

• Big data analytics, natural language processing, and deep learning for emergency medicine.

• Data sharing, data privacy, interoperability, and multi-center collaborative research using AI.

• Data curation and annotation, data quality and standardization within the context of acute care environments.

• Human-in-the-loop workflows, and human-machine collaborative approaches.

4. Education, Training, and Workforce Development

• AI-enhanced education, simulation, and competency assessment for emergency clinicians, nurses, and allied health professionals.

• Integration of AI with extended reality (XR) technologies—including virtual, augmented, and mixed reality—to provide immersive training, team-based simulations, and scenario-based learning in emergency medicine.

• Training curricula and resources on AI literacy for the emergency medicine workforce.

• Strategies to bridge the gap between AI research and clinical adoption.

5. Policy, Ethics, Equity, and Global Perspectives

• Legal, ethical, and regulatory issues in the implementation and governance of AI in emergency medicine.

• Addressing bias, fairness, transparency, and explainability of AI systems.

• Global health applications, and reducing disparities in emergency care delivery using AI.

• Potential risks and harms of applying AI in emergency medicine.

6. Technology, Innovation, and Translational Science

• Emerging AI technologies, sensors, robotics, and novel devices for acute care.

• Translational research from bench to bedside, including pilot studies, clinical trials, and real-world implementations.

• Partnerships between academia, industry, and healthcare systems to advance innovation.

7. Case Studies, Quality Improvement, and Implementation Science

• Reports on successful implementation, challenges, failures, and lessons learned from deploying AI in emergency settings.

• Impact studies on patient outcomes, safety, quality, and operational efficiency.

AIEM seeks to become the premier international open access platform for knowledge exchange at the intersection of AI and emergency medicine. By embracing a broad, interdisciplinary scope, the journal aims to accelerate scientific progress, inform best practices, and shape the future of emergency care worldwide.