We asked the questions you want to: Why is imaging ripe for AI? How will improvements in image processing and reconstruction, quality control and work list prioritization improve the practice of radiology? 

Learn how ML algorithms are helping radiologists to improve diagnosis, find more cancers, reduce biopsies and increase efficiency, and what IT departments need to know to deploy AI apps.

Maximize your IT investments by sharing diagnostic images and reports. Listen to physicians and hospital leaders discuss how they are meeting this challenge.

See how leading radiologists and oncologists at NIH are using technology such as semi-automated cancer lesion management and multi-media reporting software to reliably, consistently and quickly distribute reports in oncology follow up and treatment planning.

With digital radiology, PACS and RIS in use for 15 years or more at most US hospitals, a disparate system architecture is now the reality for most departments. Multiple PACS and mini-PACS populate the environment, resulting in inefficiencies, increased operating costs, and reduced throughput for departments.