The level of expectation surrounding AI in healthcare has reached fever pitch in recent years, with a number of pilot projects achieving positive early results. Most of these projects involved AI systems being trained on a sample dataset of medical data, such as x-rays or other medical imagery, after which the system was capable of providing early detection of various conditions. The challenge for many of these systems is that they were usually trained on data from a single healthcare provider, with a common health IT system. A recent study highlights how when faced with data from different health systems, such AI technologies often perform much worse than doctors. The research, conducted by the Icahn School of Medicine at Mount Sinai, used convolutional neural networks (CNNs) to analyze a bunch of chest X-ray images with the aim of diagnosing pneumonia.