Dr. Lundstrom said that advances in artificial intelligence and non-invasive brain-stimulation techniques could allow clinicians to monitor cortical excitability in real time [1].

This development is significant because it may allow doctors to predict which therapies will work for specific patients before they undergo treatment. By identifying effective interventions early, clinicians could reduce the time patients spend failing treatments that do not work for their specific brain chemistry.

The approach focuses on the use of non-invasive brain-stimulation, such as transcranial magnetic stimulation (TMS), to assess how the brain responds to stimuli [1]. Dr. Lundstrom said that integrating AI with these techniques helps researchers understand brain function more accurately. The goal is to create a predictive model that matches a patient's neurological profile with the most effective therapy.

These tools are being developed to address a variety of neurological conditions. The technology is particularly relevant for patients suffering from epilepsy, chronic pain, and various mood disorders [1]. By monitoring cortical excitability, doctors can see how the brain's neurons react and adjust stimulation parameters accordingly.

Currently, many neurological treatments rely on a trial-and-error process. Dr. Lundstrom said that the shift toward AI-driven prediction could move the field toward a more personalized medicine approach. This transition would allow for the customization of brain-stimulation protocols based on real-time data rather than general population averages.

The integration of AI allows for the processing of complex data sets that human clinicians cannot analyze manually in real time [1]. This capability is essential for capturing the dynamic nature of brain activity during stimulation sessions.

“AI and non-invasive brain-stimulation could let clinicians monitor cortical excitability in real time.”

The intersection of AI and neuromodulation represents a shift from reactive to predictive neurology. If clinicians can accurately map cortical excitability and use AI to simulate treatment outcomes, the standard of care for chronic neurological conditions could move away from empirical trial-and-error toward a precision-medicine model.