Kevin Haines, D.O., PGY-4 from University of Connecticut Health Center, shares an insightful recap of the 2024 Quality and Safety + Informatics Confer...
The ACR Quality and Safety + Informatics Conference provides a unique forum for quality and safety professionals to collaborate with informaticists, d...
This briefing is part of our series summarizing key research and articles to keep you up to speed on the latest in medical imaging AI.
According to the Center for Disease Control (CDC) statistics, there are about 260,000 newly diagnosed cases of breast cancer in the United States with...
A thoughtful approach to implementing AI tools can prevent adverse effects that can lead to disparities in healthcare.
Photoacoustic (PA) Imaging is an emerging imaging modality that enables imaging via optical absorption in biological tissues with acoustic resolution....
Synthetic data has the potential to fill research gaps, but radiologists should be aware of potential drawbacks.
This briefing in our quarterly series summarizing key research and articles on AI will bring you up to speed on how AI goes from concept to value.
There are unanswered questions about what happens in situations where the human expert disagrees with AI, and how those situation are perceived or pot...
An advance look at the topics of the ACR’s 2022 Imaging Informatics Summit
While the ramifications of using AI on pediatric patients aren’t yet clear, the need for advocacy is.
This first briefing in our new quarterly series summarizing key research and articles on AI will bring you up to speed on how we are learning from lat...
The 2022 Data Science Summit delved into the complex relationship between AI expectations and strategies for successful AI implementation.
While de-identification of data is notoriously difficult, there are several methods helping radiology applications protect patient privacy and making ...
You don’t need to be a full-time data scientist to keep abreast of the newest technologies.
When it comes to potential issues with AI models, radiologists must be vigilant.
Most AI tools focus on a specific problem, slowing AI adoption and frustrating radiologists.