Integrating Artificial Intelligence into the Diagnosis of Neurological Diseases in Traditional Chinese Medicine

Abstract

Traditional Chinese medicine (TCM) uses a holistic approach and syndrome differentiation to diagnose neurological disorders. However, traditional diagnostic methods—which rely heavily on subjective clinical experience—lack objective standardization. This paper reviews the transformative application of artificial intelligence (AI) in modernizing TCM diagnostics for neurological diseases. By integrating advanced technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP), AI provides unprecedented objectivity and precision. Current applications demonstrate significant breakthroughs, notably in the use of deep learning to analyse extensive clinical records for automated syndrome differentiation (e.g., stroke patients) and the use of image recognition for automated tongue diagnosis. These innovations are shifting TCM from an experience-dependent paradigm to a data-driven model. Looking forward, the future of AI in TCM neurology hinges on multimodal data fusion, which integrates imaging, tongue, pulse, and clinical data to digitally reconstruct the TCM diagnostic process. Furthermore, developing explainable AI (XAI) is critical to overcoming the “black box” dilemma, thereby fostering clinician trust. The widespread deployment of these intelligent systems via cloud computing holds immense potential for grassroots healthcare, although it necessitates robust ethical and legal frameworks to ensure data privacy. Ultimately, AI significantly accelerates the scientific validation of TCM, paving the way for personalized and precision medicine in treating neurological conditions.