Obstacles to Healthcare Training Data Accuracy and How to Overcome Them

One of the biggest problems with AI solutions in healthcare right now is getting quality data that you can use to train your AI models. When you train an AI model using generic data, it is like a child trying to teach another child. For the AI models to be effective, they require the highest quality data including meta data that helps the model to understand the context of the data. This is particularly true in healthcare which has extremely complex data models, superfluous data, and unique terminology. The good news for healthcare IT companies is that there are companies who can help them clean up and annotate their data so that it can be used effectively in tools like ambient clinical voice and other healthcare AI solutions. One of those companies that provides human-driven data annotation services is called Xelex.ai. I wanted to learn more about the services they offered, so I sat down with Mark Christensen, CEO at Xelex.ai. Learn more about Xelex.AI: https://xelex.ai/ Learn more about WebChartMD: https://www.webchartmd.org/ Health IT Community: https://www.healthcareittoday.com/