Artificial Intelligence in Traditional Chinese Medicine: Multimodal Fusion and Machine Learning for Enhanced Diagnosis and Treatment Efficacy

Abstract

Artificial intelligence (AI) serves as a key technology in global industrial transformation and technological restructuring and as the core driver of the fourth industrial revolution. Currently, deep learning techniques, such as convolutional neural networks, enable intelligent information collection in fields such as tongue and pulse diagnosis owing to their robust feature-processing capabilities. Natural language processing models, including long short-term memory and transformers, have been applied to traditional Chinese medicine (TCM) for diagnosis, syndrome differentiation, and prescription generation. Traditional machine learning algorithms, such as neural networks, support vector machines, and random forests, are also widely used in TCM diagnosis and treatment because of their strong regression and classification performance on small structured datasets. Future research on AI in TCM diagnosis and treatment may emphasize building large-scale, high-quality TCM datasets with unified criteria based on syndrome elements; identifying algorithms suited to TCM theoretical data distributions; and leveraging AI multimodal fusion and ensemble learning techniques for diverse raw features, such as images, text, and manually processed structured data, to increase the clinical efficacy of TCM diagnosis and treatment.

Keywords: Artificial intelligence; Clinical Efficacy; Clinical dignosis; Deep learning; Ensemble learning; Machine learning; Multimodal fusion; Prescription generation; Syndromic elements; Traditional Chinese medicine.