Insights

This week’s Domain Knowledge highlights the top 5 takeaways regarding the use of AI in radiology.

As AI reshapes the radiology landscape, it presents both new opportunities and challenges to the radiology education system. In this blog post, we'll delve into how radiology educators and trainees will be asked to adapt to the era of AI.

This week’s Domaine Knowledge has five healthcare professionals sharing why they’re thankful for the use of AI in their clinical practice.

In this blog post, we will explore how AI radiology services address burnout issues by optimizing workflow, improving image quality, and helping reduce the burden on radiologists. We will highlight current tools, such as AIR Recon DL and Rad AI Nexus, to illustrate how they contribute to workload optimization.

Patient-centered radiology represents a holistic approach to medical imaging that considers and prioritize patients’ individual needs and concerns. Artificial intelligence (AI) can play a major role in improving the patient experience in radiology.

For this week’s Domain Knowledge, we scanned industry research and news to identify the top 10 uses of AI in healthcare.

Having discussed how quality can be defined for your institution, in this blog post, we'll delve into how various stakeholders design quality and safety standards for the industry. We'll aslo look at how to ensure that you, as a radiologist, are equipped with the knowledge and tools necessary to provide the highest standard of patient care amidst the challenges and opportunities presented by the emergence and adoption of AI in breast imaging.

Ferrum’s AI-powered platform supports reading radiologists to help identify diseases like cancer in the early stages.

“Our partnership with Sutter has played an instrumental role where our technology provides a positive and lasting impact on the lives of patients across Northern California,” Pelu Tran, CEO of Ferrum Health, said in a statement provided by Sutter.