Despite advancements in AI and data acquisition, brain features are underutilized in clinical settings for diagnosis. The authors argue that conventional methods relying on single data types are inadequate due to the complex and degenerative nature of brain features. They propose a shift towards utilizing multimodal data that includes brain activity, neurotransmitters, and imaging to redefine how biomarkers are identified.
By focusing on longitudinal studies and the integration of diverse brain data types, researchers can better understand the multifaceted nature of brain diseases. This approach would not only enhance the specificity of biomarkers but also potentially lead to earlier and more accurate diagnoses. The implications of this shift could significantly improve patient outcomes and the overall effectiveness of clinical interventions in neuropsychiatry.
👉 Pročitaj original: arXiv AI Papers
