Electroencephalography (EEG) and magnetic resonance imaging (MRI)

as neuroimaging technologies

provided objective and visualized technical tools for analyzing the brain effect mechanisms of acupuncture and moxibustion from the perspectives of brain structure

function

metabolism

and hemodynamics. The advancement of artificial intelligence (AI) algorithms can compensate for issues such as the large and scattered nature of neuroimaging data

inconsistent quality

and high heterogeneity of image information. The integration of AI with neuroimaging can facilitate individualized

intelligent

and precise prediction of acupuncture and moxibustion effects

enable intelligent classification of differential acupuncture responses

and identify brain activation patterns. This paper focuses on EEG and MRI

analyzing how machine learning and deep learning optimize multimodal neuroimaging data and their applications in the study of acupuncture and moxibustion brain effects mechanisms. Furthermore

it highlights current research gaps and limitations to provide insights for future studies on acupuncture brain effects mechanisms.