We'll discuss the Clara Platform, which is designed to bring NVIDIA technology and expertise in high performance computing, artificial intelligence, and photorealistic rendering to the medical-imaging industry. Our talk will focus on how developers from industry and institutions are leveraging the platform to integrate artificial intelligence into hospitals to bend the cost curve and improve patient outcomes.
The increasing availability of large medical imaging data resources with associated clinical data, combined with the advances in the field of machine learning, hold large promises for disease diagnosis, prognosis, therapy planning and therapy monitoring. As a result, the number of researchers and companies active in this field has grown exponentially, resulting in a similar increase in the number of papers and algorithms. A number of issues need to be addressed to increase the clinical impact of the machine learning revolution in radiology. First, it is essential that machine learning algorithms can be seamlessly integrated in the clinical workflow. Second, the algorithm should be sufficiently robust and accurate, especially in view of data heterogeneity in clinical practice. Third, the additional clinical value of the algorithm needs to be evaluated. Fourth, it requires considerable resources to obtain regulatory approval for machine learning based algorithms. In this workshop, the ACR and MICCAI Society will bring together expertise from radiology, medical image computing and machine learning, to start a joint effort to address the issues above.
AI in medical imaging has the potential to provide radiology with an array of new tools that will significantly improve patient care. To realize this potential, AI algorithm developers must engage with physician experts and navigate domains such as radiology workflow and regulatory compliance. This session will discuss a pathway for clinical implementation, and cover ACR's efforts in areas such as use case development, validation, workflow integration, and monitoring.