[Research progress in the integration of machine learning and acupunctology]

Area of Science:

• Integrative Medicine
• Computational Medicine
• Traditional Chinese Medicine

Background:

• Machine learning (ML) is a rapidly advancing field with significant impact across various sectors, including medicine.
• Traditional Chinese Medicine (TCM), particularly acupuncture and moxibustion, offers targeted therapeutic benefits.
• Integrating ML into acupuncture and moxibustion can optimize TCM resource allocation and clinical practice.

Purpose of the Study:

• To explore the synergistic integration of machine learning (ML) with acupuncture and moxibustion.
• To analyze recent literature on ML applications in acupuncture and moxibustion.
• To identify future directions for ML in this field.

Main Methods:

• Literature review and analysis of recent research combining ML with acupuncture and moxibustion.
• Discussion of ML applications in acupoint selection, prescription optimization, and clinical teaching.
• Exploration of ML's role in predicting treatment efficacy.

Main Results:

• ML integration offers potential for targeted therapy in acupuncture and moxibustion.
• Applications span acupoint selection, prescription formulation, clinical education, and treatment outcome prediction.
• The combination moves beyond data mining to enhance clinical service and resource allocation.

Conclusions:

• Machine learning presents a powerful tool to advance acupuncture and moxibustion practices.
• Further integration can refine TCM resource management and clinical decision-making.
• Multidisciplinary collaboration is key to unlocking the future potential of ML in acupuncture and moxibustion.