Researchers at the Medical University of South Carolina are working on a new approach to help smokers quit using repetitive transcranial magnetic stimulation. This technique uses electromagnetic pulses to affect brain activity. The team used machine learning, a form of AI, to analyze brain images and predict which smokers would benefit most from treatment.
They found that the salience network, which filters information to determine what's important, plays a crucial role in smoking behavior. By identifying this network's connectivity, researchers can better target treatment. According to the study leader, this approach could lead to more effective and personalized treatment for smokers.
The study used functional magnetic resonance imaging to detect changes in blood flow and measure brain activity. The researchers believe that this study lays the groundwork for larger studies to further explore targeted treatment for smokers.
By using AI-driven data analysis, medical experts hope to develop smarter, adaptive treatments that address the unique needs of each patient. This could potentially improve the effectiveness of the treatment and reduce side effects.
