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 M^{1, 2} ; Eshraghi R³ ; Behzad S⁴ ; Tavasol A² ; Bahrami A³ ; Tafazolimoghadam A⁵ ; Bhatt V⁶ ; Ghadimi D² ; Gholamrezanezhad A^{7, 8}
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.