introduction
Application of AI in emergency medical care
The Benefits of AI in Emergency Care
Issues and limitations
Conclusion
reference
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Artificial intelligence transforms emergency care by enhancing triage, diagnosis, and resource management, and faces challenges related to ethics, bias and regulation. This article explores the applications, benefits, and limitations of real-world clinical care.
Image credit: jhevphoto/shutterstock.com
introduction
Artificial Intelligence (AI) is an interdisciplinary discipline that integrates computer science, mathematics, and related fields to create algorithms that can perform tasks that are limited to traditional human intelligence. AI algorithms utilize data-driven analysis, stochastic modeling, and iterative optimization to learn, solve problems and make decisions.1
Unprecedented computing power, widely available and open access electronic health data, and algorithm breakthroughs are rapidly shifting AI from conceptual technology to the latest integrated components of healthcare.1 Despite the forecast growth of the global AI healthcare market, the technology's relative capabilities and lack of standardization remain limited in place in clinical practice.2
In emergency care, AI is attracting attention not only for clinical decision support (CDS), but also for its integration with digital twin modeling of patients, predictive analysis of emergency department (ED) flows, and pre-hospital emergency medical services (EMS).3,8,9
Furthermore, recent primers highlight the importance of accustoming non-knowledge clinicians with AI principles, terminology and limitations to support safe and informed adoption.11
Application of AI in emergency medical care
AI-driven triage algorithms can analyze large datasets at much deeper depths than traditional models without bias, allowing clinicians to prioritize patients more effectively compared to traditional methods.5 In fact, machine learning models consistently demonstrate excellent discrimination and performance capabilities to predict emergency outcomes such as hospitalization and intensive care unit (ICU) relocation and conditions such as stroke, sepsis, and myocardial infarction.4,5
Medical imaging and interpretation of these images is one of the most mature applications of AI, as numerous deep learning algorithms are trained to analyze x-rays, computed tomography (CT) scans, and ultrasound images.1 In these applications, AI technology has succeeded in supporting clinicians with high accuracy with abnormalities such as intracranial hemorrhage, fractures, and pneumonia surgery, reducing traditional diagnostic delays.1 Explanatory AI (XAI) methods are increasingly incorporated into these models to increase clinician confidence by making diagnostic output more interpretable.7,11
AI-powered CDS systems are developed to integrate real-time data from electronic health records (EHRS) and provide timely recommendations.1 For example, AI models are used to analyze electrocardiograms (ECGs) to predict imminent cardiac arrest. Machine learning support alerts have also been shown to improve antibiotic administration time.1 More recently, the scoping review highlights that emergency department CDS tools are being used to improve sepsis management, diagnosis accuracy, and disposal planning.3 Examples of published cases include Duke's “sepsis clock” system and viz.ai for fibroma detection, indicating real-world clinical adoption.11
AI-based predictive analytics can reduce ED congestion by predicting patient arrivals and surges. This application of AI allows hospitals to move from reactive to aggressive staffing models that ensure optimal allocation of limited resources such as beds.1,6
AI-powered symptom checkers and chatbots can simultaneously guide patients to self-assess the urgency of their condition. Emergency dispatchers can also use natural language processing to recognize conditions such as cardiac arrest outside the hospital.1 The EMS application includes AI-driven decision support for ambulance routing to improve patient outcomes before hospital arrivals, pre-hospital risk stratification, and remote monitoring.6,11
Another new domain is the use of a digital twin, virtual patient model, which simulates the progression of digital tension and the treatment response. This helps personalize emergency medical interventions and optimize resource use.9
The Benefits of AI in Emergency Care
AI algorithms can quickly process and synthesize huge amounts of data, leading to faster and more accurate evaluations.4 This significantly reduces the latency of traditional image interpretation, and some AI models demonstrate performance outperforming human expert performance for a given task.1
AI can offer several benefits to existing public health infrastructure. By accurately predicting patient volume, AI can enable hospitals to better manage patient throughput, reduce system inefficiencies, reduce overcrowding, and reduce patient waiting times.6 These forecasting tools support disaster preparedness and surge capacity planning, enhancing system resilience.4,5
For administrative purposes, AI can automate routine and time-consuming tasks using surrounding listening technologies and generative AI-based clinical summary. The adoption of AI in these aspects of healthcare not only reduces clinician burnout, but also improves both patient satisfaction and provider well-being.1,4 Furthermore, AI can promote continuous quality improvement by identifying patterns of adverse events and enabling evidence-based policy development.7,11
High-tech hospitals use artificial intelligence in patient care
Issues and limitations
Despite future promises and validated benefits, AI integration into emergency care is associated with many technical, ethical, and legal challenges that must be addressed to ensure safe and equitable deployment.1,4,6
The fundamental principle of machine learning is that models are as good as the data they are trained to. Thus, models trained on historical health data that contain potential biases, such as social inequality and ungeneralized sampling designs, can learn and amplify these biases at scale.6 Unfortunately, these underestimated are often precise subpopulations of patients, such as women, racial minorities, and other marginalized groups that benefit most from AI integration.2
An important practical barrier, especially in developing and underdeveloped regions, is the difficulty of integrating new AI systems into existing, often fragmented hospital intelligence technology (IT) infrastructures. The lack of data interoperability between different EHR systems makes it difficult to seamlessly integrate AI solutions, which can increase implementation complexity and associated costs.1 Even in advanced settings, CDS systems face challenges in integrating workflows and hiring clinicians, which could limit the actual impact.3,11
AI models require access to a large dataset of patient sensitive patient information. This poses a significant risk to patient privacy and data security.6,7 This is exacerbated by the “black box” issue. In this issue, the internal decision-making process of complex deep learning models is opaque and not easily interpreted. Therefore, explanability and transparency are important to support clinical accountability and legal medical decision-making.7,11
Regulatory concerns are becoming increasingly important. AI tools (SAMDs) classified as software require evidence of safety, efficacy, and lifecycle monitoring and fall under US FDA surveillance.11
Self-satisfaction of automation, reflecting an overreliance on AI, and selective adherence to accept only advice that confirms existing beliefs, represent practical and continuing challenges in clinical interactions.1
Image credits: sutipond somnam / shutterstock.com
Conclusion
AI represents the transformational power of emergency care with the potential to accelerate and improve the accuracy of patient triage, diagnosis, and resource management, thereby leading to a more efficient and resilient global emergency care system. Nevertheless, naive and inherent limitations associated with AI emphasize the importance of using this technology as a tool to enhance and empower human clinicians rather than replacing or undermine them. Future directions include broader assessments of digital twins, real-world verification of CDS systems, EMS-centric AI interventions, and education of anonymous clinicians. This is key to realizing the possibilities of AI in emergency medical care.1,3,8,9,11
The role of digital twins in the transformation of emergency medical care.9
As these technologies continue to advance and become more accessible, policymakers, regulators, and healthcare leaders must work together to create a robust ethical and legal framework that provides clear guidance on data privacy, algorithm transparency, and legal liability. These efforts ensure that the principles of safety, equity and accountability lead to a gradual deployment of AI into the global healthcare sector.
reference
- Chenais, G., Lagarde, E. , & Gil-Jardiné, C. (2023). Artificial intelligence in emergency care: Current application perspectives and predictable opportunities and challenges. Journal of Medical Internet Research25, E40031. doi:10.2196/40031, https://www.jmir.org/2023/1/E40031
- Bajwa, J., Munir, U., Nori, A. , and Williams, B. (2021). Artificial Intelligence in Healthcare: Transforms medical practices. Future Healthcare Journals, 8(2), E188-E194. doi:10.7861/fhj.2021-0095, https://www.sciencedirect.com/science/article/pii/S2514664524005277?via%3dihub
- Kareemi, H., Yadav, K., Price, C. et al. (2025). Artificial intelligence-based clinical decision support in the emergency department: a scoping review. Academic emergency medical care, 32(4), 386-395. doi:10.1111/acem.15099, https://onlinelibrary.wiley.com/doi/full/10.1111/acem.15099
- Da'costa, A., Teke, J., Origbo, J., et al. (2025). AI-led triage in the emergency department: a review of benefits, challenges and future directions. International Journal of Medical Informatics, 197105838. doi:10.1016/j.ijmedinf.2025.105838, https://www.sciencedirect.com/science/article/pii/S1386505625000164
- Piliuk, K. , and Tomforde, S. (2023). Artificial intelligence in emergency medical care. Systematic literature review. International Journal of Medical Informatics, 180105274. doi:10.1016/j.ijmedinf.2023.105274, https://www.sciencedirect.com/science/article/pii/S1386505623002927
- Rosemaro, E., Anasica, & Zellar, I. (2025). A decision support system for AI-based emergency medical services. International Journal of Recent Advances in Engineering and Technology, 13(1), 6-10. https://journals.mriindia.com/index.php/ijraet/article/view/55
- Al Kuwaiti, A., Nazer, K., Al-Reedy, A., et al. (2023). A review of the role of artificial intelligence in healthcare. Journal of Personalized Medicine, 13(6), 951. doi:10.3390/jpm13060951, https://www.mdpi.com/2075-4426/13/6/951
- Li, F., Ruijs, N. , and Lu, Y. (2022). Ethics & AI: A systematic review of ethical concerns and related strategies for designing with AI in healthcare. ai, 4(1), 28-53. doi:10.3390/ai4010003, https://www.mdpi.com/2673-2688/4/1/3
- Li, H., Zhang, J., Zhang, N. , & Zhu, B. (2025). Advances in emergency medical care through digital twins. JMIR Aging, 8E71777. doi:10.2196/71777, https://aging.jmir.org/2025/1/e71777/
- Smith, me, Zareski, CC, Lee, S. , Gottlieb, M. , Adhikari, S., Goebel, M., Wegman, M., Garg, N., Lam, S. F. (2025). Artificial Intelligence in Emergency Medicine: An Introduction to Nonexpert. Jacep Open6, 100051. doi: 10.1016/j.acepjo.2025.100051, https://www.sciencedirect.com/science/article/pii/S2688115225000098
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Last updated: September 15, 2025
