Healthcare IT Today June 28, 2022
The following is a guest article by Vanessa Braunstein, Healthcare Product Marketing Lead at NVIDIA.
Building great AI models in healthcare and life sciences requires lots of data that is diverse, well-labeled, and spans across different patient types.
However, as AI gains traction, there are still a number of bottlenecks that slow down the process of developing robust AI models such as patient privacy, access to data, and lack of clinical expertise to annotate data for training.
In order to overcome these barriers, data scientists and developers have developed new solutions such as federated learning paradigms, AI models that require less labeled data to reach state-of-the-art performance, and AI models that generate synthetic clinical data which can be used to...
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