Fathi Mobina, Eshraghi Reza, Behzad Shima, Tavasol Arian, Bahrami Ashkan, Tafazolimoghadam Armin, Bhatt Vivek, Ghadimi Delaram, Gholamrezanezhad Ali
Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.
School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Emerg Radiol. 2024 Dec;31(6):887-901. doi: 10.1007/s10140-024-02278-2. Epub 2024 Aug 27.
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.
人工智能（AI）及其近期在医疗保健领域日益深入的整合，在放射学和医学成像实践中既带来了新机遇，也带来了挑战。人工智能技术的最新进展提高了工作效率、提升了诊断准确性，并全面改善了患者护理。然而，人工智能存在数据不平衡、算法性质不明确以及某些疾病检测困难等局限性，这使其难以广泛应用。本文综述了使用人工智能模型诊断颅内出血、脊柱骨折和肋骨骨折的案例，同时讨论了诸如类型、位置、大小、伪影的存在、钙化以及术后变化等因素如何影响人工智能模型的性能和准确性。虽然使用人工智能有潜力改善急诊放射学实践，但在确保患者总体安全的同时，解决其局限性以最大限度发挥其优势也很重要。
