Creating Personalised Neuromedicine Using Artificial Intelligence and Brain Modelling
The utility of data in medicine is rapidly increasing, due to better precision and greater availability. To maximise the impact this will have, 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 the clinical neurosciences, we can apply modelling insights to patient care, on an individual level, by using artificial intelligence. Through the intelligent analysis of neuroimaging and other 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. Outlined through case studies are some recent advances in the field of personalising medicine using computational neurodiagnostics, and how they have been performed. In addition to the state of the art, the importance of this new field will be discussed, as well as how it will affect medicine, and where will it be headed in the future.
Dalton is a neuroscience researcher affiliated with the Laboratory for Computational Neurodiagnostics, in Stony Brook University. He applies computational methods, primarily based on brain modelling, to answer questions in neuroscience and psychiatry.