Challenges with Publicly Available CXR Datasets
In 2018, RSNA announced the Pneumonia Detection Challenge, calling participants to build an AI algorithm to automatically locate lung opacities on CXR images. To support the Kaggle competition, the US National Institutes of Health provided a large publicly available chest X-ray dataset, Chest X-ray 14 (CXR14). The dataset contained a total of 108,948 frontal view CXR images from 32,717 patients.
Autonomous AI and the FDA
During the Food & Drug Administration’s Public Workshop on the Evolving Role of Artificial Intelligence in Radiological Imaging, Dr. Nicholas Petrick, PhD, of the FDA discussed pre-market and post-market evaluation of autonomous AI and machine learning, and shared lessons from the Agency’s experience regulating CAD devices. On one of his slides, shown below, he describes a very similar scenario to the one we are discussing–having AI screen out normal exams, leaving the abnormal exams for human interpretation.
How Do We Get There?
This resource was first published prior to the 2020 rebranding of CuraCloud to Keya Medical. The content reflects our legacy brand.