Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Potential Strength and Weakness of Artificial Intelligence Integration in Emergency Radiology: A Review of Diagnostic Utilizations and Applications in Patient Care Optimization Publisher Pubmed



Fathi M1, 2 ; Eshraghi R3 ; Behzad S4 ; Tavasol A2 ; Bahrami A3 ; Tafazolimoghadam A5 ; Bhatt V6 ; Ghadimi D2 ; Gholamrezanezhad A7, 8
Authors
Show Affiliations
Authors Affiliations
  1. 1. Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
  2. 2. School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  3. 3. Student Research Committee, Kashan University of Medical Science, Kashan, Iran
  4. 4. Independent Researcher, Tehran, Iran
  5. 5. Tehran University of Medical Science (TUMS), Tehran, Iran
  6. 6. School of Medicine, University of California, Riverside, CA, United States
  7. 7. Keck School of Medicine of University of Southern California, Los Angeles, CA, United States
  8. 8. Department of Radiology, Division of Emergency Radiology, Keck School of Medicine, Cedars Sinai Hospital, University of Southern California, 1500 San Pablo Street, Los Angeles, 90033, CA, United States

Source: Emergency Radiology Published:2024


Abstract

Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. Recent advancements in AI technology have allowed for more workplace efficiency, higher diagnostic accuracy, and overall improvements in patient care. Limitations of AI such as data imbalances, the unclear nature of AI algorithms, and the challenges in detecting certain diseases make it difficult for its widespread adoption. This review article presents cases involving the use of AI models to diagnose intracranial hemorrhage, spinal fractures, and rib fractures, while discussing how certain factors like, type, location, size, presence of artifacts, calcification, and post-surgical changes, affect AI model performance and accuracy. While the use of artificial intelligence has the potential to improve the practice of emergency radiology, it is important to address its limitations to maximize its advantages while ensuring the safety of patients overall. © The Author(s), under exclusive licence to American Society of Emergency Radiology (ASER) 2024.
Related Docs
1. Artificial Intelligence Diagnostic Accuracy in Fracture Detection From Plain Radiographs and Comparing It With Clinicians: A Systematic Review and Meta-Analysis, Clinical Radiology (2024)
2. Cultivating Diagnostic Clarity: The Importance of Reporting Artificial Intelligence Confidence Levels in Radiologic Diagnoses, Clinical Imaging (2025)
3. Advancing Intracranial Aneurysm Detection: A Comprehensive Systematic Review and Meta-Analysis of Deep Learning Models Performance, Clinical Integration, and Future Directions, Journal of Clinical Neuroscience (2025)
4. Radiomics and Deep Features: Robust Classification of Brain Hemorrhages and Reproducibility Analysis Using a 3D Autoencoder Neural Network, Bioengineering (2024)
Experts (# of related papers)
Arad Iranmehr (1)