Creating Personalised Neuromedicine Using Artificial Intelligence and Brain Modelling
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
3 Key Takeaways:
*The new horizon of effective psychiatry and neurology is in personalised, data-driven methods.
*Machine learning and artificial intelligence provide superior methods for accomplishing this.
*Concrete implementations of this paradigm already exist in the lab, and are ready for translation into clinical settings.
Dalton is a neuroscientist and mathematician affiliated with Stony Brook University's Renaissance School of Medicine, in the Department of Biomedical Engineering. He applies mathematical methods, primarily based on brain modelling, to answer clinical questions in neuroscience and psychiatry.