AI-enabled Neurology

Recent developments in artificial intelligence and the availability of
large scale medical imaging datasets allow us to learn how the human
brain truly looks like from a biological, physiological, anatomical
and pathological point-of-view. This learning process can be further
augmented by diagnostic and radiological report data available in
clinical systems, providing an integrated view of the human
interpretation of medical imaging data. This talk will present how
these models can learn from big and unstructured data and then be used
as tools for precision medicine, where we aim to translate advanced
imaging technologies and biomarkers to clinical practice in order to
streamline the clinical workflow and improve the quality of care. This
process of technical translation requires deep algorithmic integration
into the radiological workflow, fully automated image processing,
quality control and assurance, extensive validation on clinical grade
data, and the deployment of an automated reporting system that
summarizes a complex set of imaging biomarkers, highlighting the
presence of abnormalities.