Agfa Brings Intelligent Radiography to RSNA 2018

The company demonstrated developments in progress to its Enterprise Imaging platform, previewing how it will serve as the foundation to enable machine learning and evidence-based AI enabling task-based workflow automation, image analysis and predictive analytics.

The Enterprise Imaging platform removes traditional barriers to medical imaging, enables multi-specialty care team collaboration and enhanced cross-platform mobility. With embedded advanced applications and AI, Agfa's Enterprise Imaging solution will provide radiologists with additional information for decision-making, speedy access to results and analytics helping significantly improve the patient's care journey and satisfaction, as well as the organization's clinical, operational and financial well-being.

With the task-based rules engine, the aggregation of both imaging and non-imaging data from disparate healthcare information systems, and strong collaboration tools, radiologists can more efficiently make evidence-based decisions and recommendations, and play an enhanced role as consultant for physicians and patients. An approach to precision medicine, analytical intelligence and aggregation of imaging and non-imaging data at the Radiology Desktop will offer patient-centered personalized care delivery, based on analysis of outcomes of similar patient population and data sets.

"Whether detecting cancers or treating chronic diseases, machine learning and advanced analytics will help radiologists and diagnosticians focus less on manual tasks and more on improving care pathways," said Dr. Anjum Ahmed, Global Director Imaging Information Systems at Agfa HealthCare. "Agfa HealthCare's enhancements to the Enterprise Imaging platform, currently in development, will serve as the foundation to enable machine learning and evidence-based augmented intelligence. Unlike AI, which tries to replace human intelligence, augmented intelligence works with and amplifies human intelligence."

"The platform will serve as the intersection of machine learning and advanced applications, where clinical knowledge and medical data converge on a single platform. The potential benefits of augmented intelligence are realized when it is used in the context of workflows and systems that healthcare practitioners operate and interact with, hence introducing efficiencies," added Ahmed.