Revolutionizing emergency care: an overview
of the transformative role of artificial
intelligence in diagnosis, triage, and patient
management

Revolutionizing emergency care: an overview
of the transformative role of artificial
intelligence in diagnosis, triage, and patient
management

Artificial intelligence, Machine learning, Deep learning, Emergency care, Trauma

Abstract
Background The deployment of artificial intelligence (AI) applications in the healthcare domain has witnessed
a significant and noteworthy surge. This is particularly pronounced within the fast-paced and critical realm of
emergency care, where the integration of AI has manifested as a transformative force, exerting profound influence on
the diagnosis of trauma-related complications.
Objective This scholarly article aims to provide an in-depth exploration of the multifaceted applications of AI in the
emergency department, elucidating its remarkable efficacy in expediting and refining the precision of diagnoses and
patient management within this exigent setting.
Methods Through a meticulous and comprehensive review of pertinent literature, this study endeavors to delineate
and emphasize key AI applications, thereby illuminating their significant impact in optimizing patient outcomes
and rationalizing workflows within emergency care. This scholarly exploration seeks to underscore the burgeoning
potential of AI as an indispensable ally in the collective pursuit of achieving apid and accurate diagnoses, particularly
in high-stakes emergency settings.
Results Findings reveal that AI is driving a paradigm shift in emergency medicine by transforming clinical
approaches to urgent cases. Its implementation has shown substantial potential in optimizing patient outcomes and
streamlining clinical workflows.
Conclusion AI stands as a promising and indispensable tool in the pursuit of rapid and accurate diagnoses in
emergency care. Its continued integration is poised to significantly enhance clinical decision-making and patient care
in high-stakes scenarios.
Keywords Artificial intelligence, Machine learning, Deep learning, Emergency care, Trauma

Hanieh Alimiri Dehbaghi1 and Karim Khoshgard2*

https://doi.org/10.1186/s12245-025-01049-1

2025

Peri Irawan

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