THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN CARDIOPULMONARY RESUSCITATION

Authors

  • Barotov Sh.K. Bukhara State Medical Institute

Abstract

Sudden cardiac arrest is one of the most complex and urgent medical emergencies, resulting in the deaths of millions of individuals worldwide each year. During such pathophysiological events, the provision of rapid, accurate, and effective medical intervention is a critical determinant of patient survival. In this context, cardiopulmonary resuscitation (CPR) serves as a standardized and widely adopted protocol in clinical practice, comprising a set of urgent medical procedures aimed at restoring vital physiological functions.

References

Viderman, D., Abdildin, Y. G., Batkuldinova, K., Badenes, R., & Bilotta, F. (2023).

Artificial Intelligence in Resuscitation: A Scoping Review. Journal of Clinical Medicine, 12(6), 2254.

DOI: 10.3390/jcm12062254

This comprehensive review examines the applications of AI in resuscitation, including prediction of cardiac arrest, heart rhythm disorders, and post-cardiac arrest outcomes.

Heist, E. K. (2023).

Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review. Current Cardiology Reports, 25, 1391–1396.

DOI: 10.1007/s11886-023-01964-w

This article provides an overview of recent advances in AI and ML for predicting and managing sudden cardiac arrest, focusing on prehospital emergency care.

Kwon, J.-M., Lee, Y., Lee, Y., Lee, S., & Park, J. (2018).

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest. Journal of the American Heart Association, 7(13), e008678.

DOI: 10.1161/JAHA.118.008678

This study develops a deep learning algorithm to predict in-hospital cardiac arrest, demonstrating its potential in clinical settings.

Blomberg, S. N., et al. (2021).

Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial. JAMA Network Open, 4(1), e2032320.

DOI: 10.1001/jamanetworkopen.2020.32320

This randomized clinical trial assesses the impact of machine learning on dispatcher recognition of out-of-hospital cardiac arrest during emergency calls.

Banerjee, P., Bhattacherjee, S., Dasgupta, K., & Sen, S. (2022).

Performance Evaluation of Machine Learning Classifiers for Sudden Cardiac Arrest Detection. Journal of The Institution of Engineers (India): Series B, 103, 1–9.

DOI: 10.1007/s40031-022-00830-7

This article evaluates the performance of various machine learning classifiers in detecting sudden cardiac arrest, highlighting their effectiveness.

Published

2025-05-12

How to Cite

Barotov Sh.K. (2025). THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN CARDIOPULMONARY RESUSCITATION. Ethiopian International Journal of Multidisciplinary Research, 12(05), 155–159. Retrieved from https://www.eijmr.org/index.php/eijmr/article/view/3047