Artificial Intelligence in Emergency Medicine: A Literature Review
DOI:
https://doi.org/10.12775/QS.2024.33.55839Keywords
Artificial intelligence, emergency medicine, healthcare innovation, AI ethicsAbstract
IntroductionArtificial intelligence (AI) is rapidly transforming medical fields, particularly emergency medicine (EM), where timely decision-making is crucial. AI offers potential benefits in diagnostic accuracy, patient care optimization, and workflow efficiency within emergency departments (EDs). Purpose of Work
This review aims to synthesize recent advancements in AI applications within emergency medicine, focusing on diagnostic support, patient triage, clinical decision support systems (CDSS), and workflow optimization. Additionally, we highlight the potential benefits, challenges, and future directions for AI in EM. Material and Methods
A comprehensive literature search was conducted using PubMed and Google Scholar databases. We reviewed peer-reviewed articles from 2008 to 2024, focusing on AI-driven solutions in EDs. Keywords included "artificial intelligence," "emergency medicine," "machine learning," and "clinical decision support." Studies were selected based on their relevance to AI applications in EM, diagnostic improvements, and operational efficiency.
The results highlight the promising role of AI in improving diagnostic accuracy, reducing overcrowding, optimizing triage processes, and addressing clinician workload. However, challenges like ethical concerns, data bias, and the need for clinical validation remain. Further research is necessary to integrate AI more effectively in clinical practice.
References
Ahun, E., Demir, A., Yiğit, Y., Tulgar, Y. K., Doğan, M., Thomas, D. T., & Tulgar, S. (2023). Perceptions and concerns of emergency medicine practitioners about artificial intelligence in emergency triage management during the pandemic: A national survey-based study. Frontiers in Public Health, 11, 1285390. https://doi.org/10.3389/fpubh.2023.1285390
Balamuth, F., Alpern, E. R., Grundmeier, R. W., Chilutti, M., Weiss, S. L., Fitzgerald, J. C., Hayes, K., Bilker, W., & Lautenbach, E. (2015). Comparison of two sepsis recognition methods in a pediatric emergency department. Academic Emergency Medicine, 22(11), 1298–1306. https://doi.org/10.1111/acem.12814
Boonstra, A., & Laven, M. (2022). Influence of artificial intelligence on the work design of emergency department clinicians: A systematic literature review. BMC Health Services Research, 22, 669. https://doi.org/10.1186/s12913-022-08070-7
Childress, J. F., & Beauchamp, T. L. (2022). Common morality principles in biomedical ethics: Responses to critics. Cambridge Quarterly of Healthcare Ethics, 31, 164–176.
DOI: 10.1017/S0963180121000566
Chou, E. H., Wang, C. H., Hsieh, Y. L., Namazi, B., Wolfshohl, J., Bhakta, T., et al. (2021). Clinical features of emergency department patients from early COVID-19 pandemic that predict SARS-CoV-2 infection: A machine-learning approach. Western Journal of Emergency Medicine, 22(2), 244–251. https://doi.org/10.5811/westjem.2020.12.49370
Cossette-Lefebvre, H., & Maclure, J. (2022). AI’s fairness problem: Understanding wrongful discrimination in the context of automated decision-making. AI and Ethics.
DOI: 10.1007/s43681-022-00233-w
Di Sarno, L., Caroselli, A., Tonin, G., Graglia, B., Pansini, V., Causio, F. A., Gatto, A., & Chiaretti, A. (2024). Artificial intelligence in pediatric emergency medicine: Applications, challenges, and future perspectives. Biomedicines, 12(6), 1220. https://doi.org/10.3390/biomedicines12061220
Eppes, C. S., Garcia, P. M., & Grobman, W. A. (2012). Telephone triage of influenza-like illness during pandemic 2009 H1N1 in an obstetric population. American Journal of Obstetrics and Gynecology, 207(1), 3–8. https://doi.org/10.1016/j.ajog.2012.02.023
Farahmand, S., Shabestari, O., Pakrah, M., Hossein-Nejad, H., Arbab, M., & Bagheri-Hariri, S. (2017). Artificial intelligence-based triage for patients with acute abdominal pain in the emergency department: A diagnostic accuracy study. Advanced Journal of Emergency Medicine, 1(1), e5. DOI: 10.22114/AJEM.v1i1.11
Fernandes, M., Mendes, R., Vieira, S. M., Leite, F., Palos, C., Johnson, A., et al. (2020). Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing. PLOS ONE, 15(4), e0230876. https://doi.org/10.1371/journal.pone.0230876
Goto, T., Camargo, C. A., Faridi, M. K., Yun, B. J., & Hasegawa, K. (2018). Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED. American Journal of Emergency Medicine, 36(9), 1650–1654. https://doi.org/10.1016/j.ajem.2018.01.021
Hong, W. S., Haimovich, A. D., & Taylor, R. A. (2018). Predicting hospital admission at emergency department triage using machine learning. PLOS ONE, 13(7), e0201016. https://doi.org/10.1371/journal.pone.0201016
Horng, S., Sontag, D. A., Halpern, Y., Jernite, Y., Shapiro, N. I., & Nathanson, L. A. (2017). Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLOS ONE, 12(4), e0174708. https://doi.org/10.1371/journal.pone.0174708
Humayun, A., Fatima, N., Naqqash, S., Hussain, S., Rasheed, A., Imtiaz, H., et al. (2008). Patients’ perception and actual practice of informed consent, privacy, and confidentiality in general medical outpatient departments of two tertiary care hospitals of Lahore. BMC Medical Ethics, 9, 14. https://doi.org/10.1186/1472-6939-9-14
Jang, D. H. (2020). Developing neural network models for early detection of cardiac arrest in the emergency department. American Journal of Emergency Medicine, 38(1), 43–49. https://doi.org/10.1016/j.ajem.2019.02.036
Kim, J., Chang, H., Kim, D., Jang, D. H., Park, I., & Kim, K. (2020). Machine learning for prediction of septic shock at initial triage in the emergency department. Journal of Critical Care, 55, 163–170. DOI: 10.1016/j.jcrc.2019.09.024
Kirubarajan, A., Taher, A., Khan, S., & Masood, S. (2020). Artificial intelligence in emergency medicine: A scoping review. Academic Emergency Medicine, 27(8), 763-776. https://doi.org/10.1002/aem.13972
Klang, E., Barash, Y., Soffer, S., Bechler, S., Resheff, Y. S., Granot, T., et al. (2020). Promoting head CT exams in the emergency department triage using a machine learning model. Academic Radiology, 62, 153–160. DOI: 10.1007/s00234-019-02293-y
Klug, M., Barash, Y., Bechler, S., Resheff, Y. S., Zimlichman, E., & Klang, E. (2020). A gradient boosting machine learning model for predicting early mortality in the emergency department triage: Devising a nine-point triage score. Journal of General Internal Medicine, 35(1), 220–227. DOI: 10.1007/s11606-019-05512-7
Lai, L., Wittbold, K. A., Dadabhoy, F. Z., Sato, R., Landman, A. B., & Schwamm, L. H., et al. (2020). Digital triage: Novel strategies for population health management in response to the COVID-19 pandemic. Healthcare (Amsterdam), 8(4), 100493. https://doi.org/10.1016/j.hjdsi.2020.100493
Liang, W., Yao, J., Chen, A., Lv, Q., Zanin, M., Liu, J., et al. (2020). Early triage of critically ill COVID-19 patients using deep learning. Nature Communications, 11(1), 3543. https://doi.org/10.1038/s41467-020-17280-8
Mazurowski, M. A. (2019). Artificial intelligence may cause a significant disruption to the radiology workforce. Journal of the American College of Radiology, 16(8), 1077–1082.
DOI: 10.1016/j.jacr.2019.05.019
Payne, R., Darton, T. C., & Greig, J. M. (2009). Systematic telephone triage of possible “swine” influenza leads to potentially serious misdiagnosis of infectious diseases. Journal of Infection, 59(5), 371–372. DOI: 10.1016/j.jinf.2009.09.005
Raita, Y., Goto, T., Faridi, M. K., Brown, D. F. M., Camargo, C. A., & Hasegawa, K. (2019). Emergency department triage prediction of clinical outcomes using machine learning models. Critical Care, 23(1), 64. DOI: 10.1186/s13054-019-2351-7
Smith, S. W., Walsh, B., Grauer, K., Wang, K., Rapin, J., Li, J., et al. (2019). A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation. Journal of Electrocardiology, 52, 88–95.
DOI: 10.1016/j.jelectrocard.2018.12.017
Soltan, A. A. S., Kouchaki, S., Zhu, T., Kiyasseh, D., Taylor, T., Hussain, Z. B., et al. (2021). Rapid triage for COVID-19 using routine clinical data for patients attending hospital: Development and prospective validation of an artificial intelligence screening test. The Lancet Digital Health, 3(2), e78–e87. DOI: 10.1016/S2589-7500(20)30274-0
Stewart, J., Sprivulis, P., & Dwivedi, G. (2018). Artificial intelligence and machine learning in emergency medicine. Emergency Medicine Australasia, 30(6), 870–874.
DOI: 10.1111/1742-6723.13145
Tahayori, B., Chini-Foroush, N., & Akhlaghi, H. (2021). Advanced natural language processing technique to predict patient disposition based on emergency triage notes. Emergency Medicine Australasia, 33(3), 480–484. DOI: 10.1111/1742-6723.13656
Yu, J. Y., Jeong, G. Y., Jeong, O. S., Chang, D. K., & Cha, W. C. (2020). Machine learning and initial nursing assessment-based triage system for emergency departments. Healthcare Informatics Research, 26(1), 7. DOI: 10.4258/hir.2020.26.1.13
Zuiderveen Borgesius, F. J. (2020). Strengthening legal protection against discrimination by algorithms and artificial intelligence. International Journal of Human Rights, 24(10), 1572–1593. DOI: 10.1080/13642987.2020.1743976
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Gracjan Sitarek, Marta Żerek
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Stats
Number of views and downloads: 213
Number of citations: 0
