Creating personalised neuromedicine using artificial intelligence and brain modelling

This was a short keynote I gave about the application of multiscale modelling to data-driven neurology and psychiatry. A recording is available upon request.

Abstract:

Today, the utility of data in medicine is rapidly increasing, due to increased precision and availability. To maximise the impact this has, clinicians and researchers are applying unique analysis methods to these data and translating the results into patient care. One example of this is in personalising medicine, which entails learning about and responding to a patient’s unique condition. In clinical neurosciences, we can apply modelling insights to patient care, on an individual level, by using artificial intelligence and machine learning. Through the intelligent analysis of diagnostic data, we can learn about a patient’s brain, and then simulate a patient by building a personal brain model. This enables clear and correct diagnosis, investigation of treatments, and prediction of outcomes. We will discuss a couple of key case studies to explore recent advances in the field of personalising medicine using computational neurodiagnostics, and how they have been performed. Theory, methods, and concrete results will be examined.

Slides: ReWork 2021

Other details: link to website