Artificial Intelligence in Emergency Radiology: Where Are We Going?

Abstract

Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients’ lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS–PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients’ clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease’s severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.

The Applications of Artificial Intelligence in Radiology: Opportunities and Challenges

“…Testing and validation of the effectiveness and impact of AI applications in radiology is one of the most important challenges to the realization of the potential of artificial intelligence in the field. X claims that validation is the key to ensuring AI applicability and performance in a real-world clinical setting [24]. However, according to van Leeuwen et al, while many commercially available AI products for radiologists show promise, peer-reviewed evidence for their efficacy is lacking for the majority of software tools (64%) [5].…”

Combining Non-Contrast CT Signs With Onset-to-Imaging Time to Predict the Evolution of Intracerebral Hemorrhage

“…However, radiomics analysis is limited by the repeatability and reproducibility of radiomics features, which is not conducive to large-scale promotion and application in clinical practice [ 29 30 ]. Furthermore, artificial intelligence-assisted diagnosis and prediction of the disease has made significant progress in recent years [ 27 31 ]. Ma et al [ 27 ] developed an end-to-end deep learning method to automatically segment hematomas for HE prediction, with ResNet-34 achieving an excellent area under the curve (0.9267).…”

Artificial Intelligence in Ultrasound Imaging: Where Are We Now?

“…Artificial intelligence is revolutionizing medical ultrasonography, 31 particularly in intensive care settings, by improving US performance when data is processed and integrated by clinicians. [32][33][34] In emergency radiology, AI facilitates image acquisition, essential for rapid diagnosis and management. Philips' innovations, showcased at the 2023 American College of Emergency Physicians Scientific Assembly, incorporate AI in US systems, enhancing diagnostic capabilities in emergency medicine.…”