Empowering Machine Learning in Radiology

ACR AI-LAB offers radiologists tools designed to help them learn the basics of AI and participate directly in the creation, validation and use of health care AI.

DSI use cases are scenarios where AI may help improve medical imaging care. They help to ensure that AI tools provide needed information, can be implemented into workflow, and have the potential to improve patient care.

DSI has developed a Pandemic AI Response (PAIR) community to leverage ACR and other resources in the near term battle against COVID-19 and in the long term, to develop a framework to prepare our clinical radiologists and researchers to combat future infectious disease outbreaks.

Plan your machine learning project's image-labeling process in advance and consider the project's priorities before selecting software.

It will be years before image-interpretation AI delivers the results we see with non-interpretive AI today.

The argument that AI will soon replace radiologists is trendy, but it doesn’t hold up under scrutiny. AI will enhance the value we provide to patients, not replace us.

The American College of Radiology (ACR) Data Science Institute^® (DSI) released 90 clinical and non-interpretive use cases for the radiology community in November to more than double the number of freely available use cases on the DSI website to 140.

The American College of Radiology^® (ACR^®) Data Science Institute (DSI) recently released a new list of FDA-cleared artificial intelligence (AI) algorithms related to medical imaging.

This statement highlights that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders.