Integrating machine learning into acupuncture research: a scoping review

Most studies reported encouraging predictive performance, indicating that machine learning methods can be effectively applied to acupuncture efficacy prediction. Support vector machines, in particular, demonstrated significant potential. These findings suggest that machine learning could improve the precision and efficiency of acupuncture treatments and help create more personalized and effective treatment plans. However, small sample sizes, methodological heterogeneity, inconsistent data types, and lack of standardized datasets limit model generalizability and comparability. Important gaps remain, including mechanistic understanding, long-term outcome prediction, and evaluation of clinical impact. Future research should focus on larger, multi-center studies with standardized protocols, rigorous external validation, and assessment of clinical utility to advance the integration of machine learning into acupuncture practice.