University of Wisconsin–MadisonMedical imaging analysis

“We realized that in order to integrate AI into our workflows, we need to have two things: a lot of data and the right computing infrastructure. In healthcare, you’re faced with limited data, and in addition, the data you have might not be representative of the population. Furthermore, data often comes from disparate data sources, be it multiple vendors or from different systems like PACS, EMR, or radiology dictation software. You’re also faced with irreproducibility of studies and subjectivity of interpretation due to no ground-truth analysis, as radiologists' interpretations are often not black and white. Beyond the concerns for limited, imbalanced data, as well as the need for infrastructure to handle large, complex data, it was equally important for us to have tools to make AI training easy, portable, and reproducible as we roll out our research into daily clinical practice.”