Transforming emergency medicine with artificial intelligence: From triage to clinical decision support

Nigil kuttan; Aditya Pundkar; Charuta Gadkari; Aniket Patel; Abhishek Kumar

doi:10.31893/multirev.2025285

Keywords:

Abstract

Emergency medicine is undergoing a significant transformation due to the integration of artificial intelligence (AI), which is enhancing patient care, boosting operational efficiency, and revolutionizing clinical decision-making. This analysis examines the present applications and prospects of AI in emergency medicine, with a focus on its capacity to enhance diagnostic precision, improve triage systems, and tailor treatment strategies. Emergency departments worldwide are increasingly adopting AI-driven tools, including advanced triage systems, predictive analytics, and automated diagnostic support. These technologies have shown impressive abilities in medical image analysis, patient outcome prediction, and clinical documentation assistance. Nevertheless, the implementation of AI in emergency medicine faces obstacles such as data accessibility and quality, ethical issues, and the need for comprehensive regulatory frameworks. To ensure responsible AI system development and deployment, collaboration among healthcare professionals, data scientists, ethicists, and policymakers is essential. Future AI advancements in emergency medicine are expected to include improved predictive analytics, precise diagnostics, and individualized patient care. AI-enabled remote monitoring and telehealth services also show potential for alleviating pressure on emergency services and improving patient outcomes. As AI technology progresses, it is vital to address the constraints and challenges associated with its implementation, including data sharing, model interpretability, and potential biases. Ongoing research and stakeholder discussions are crucial to fully leverage AI's potential in emergency medicine while prioritizing patient safety, privacy, and equitable access to healthcare services.
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How to cite

kuttan, N., Pundkar, A., Gadkari, C., Patel, A., & Kumar, A. (2025). Transforming emergency medicine with artificial intelligence: From triage to clinical decision support . Multidisciplinary Reviews, 8(10), 2025285. https://doi.org/10.31893/multirev.2025285

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[2] Alamgir, A., Mousa, O., & Shah, Z. (2021). Artificial intelligence in predicting cardiac arrest: Scoping review. JMIR Publications, 9 (12), e30798. https://doi.org/10.2196/30798

[3] Alami, H., Rivard, L., Lehoux, P., Hoffman, S. J., Cadeddu, S. B., Savoldelli, M., Samri, M., Ahmed, M. A. A., Fleet, R., & Fortin, J. (2020). Artificial intelligence in health care: Laying the foundation for responsible, sustainable, and inclusive innovation in low- and middle-income countries. Globalization and Health, 16 (1), 1–12. https://doi.org/10.1186/s12992-020-00584-1

[4] Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A., Almohareb, S. N., Aldairem, A., Alrashed, M., Saleh, K. B., Badreldin, H. A., Yami, M. S. A., Harbi, S. A., & Albekairy, A. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23 (1), 1–15. https://doi.org/10.1186/s12909-023-04698-z

[5] Bajwa, J., Munir, U., Nori, A. V., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8 (2), e188–e194. https://doi.org/10.7861/fhj.2021-0095

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[7] Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In F. Allhoff, A. Lin, & J. Moor (Eds.), The Cambridge Handbook of Artificial Intelligence (pp. 316–334). Cambridge University Press . https://doi.org/10.1017/cbo9781139046855.020

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