Fusion of Artificial Intelligence and Mind-Body Medicine for Holistic Health: A Systematic Review.

BACKGROUND

Mind-Body Medicine (MBM) is an integrative healthcare approach that employs practices such as meditation, yoga, biofeedback, and Tai Chi to promote well-being. Artificial Intelligence (AI) is increasingly used in MBM to offer data-driven, real-time, and adaptive interventions. While the impact of AI on diagnostics and treatment in healthcare is well documented, its efficacy and challenges in MBM are less studied.

OBJECTIVE

This study critically evaluates the effectiveness of AI-based MBM interventions in improving mental and physiological health outcomes, compares AI-based MBM with traditional MBM interventions, compares the performance of different AI models in MBM interventions, and identifies data privacy challenges, AI explainability, and ethical concerns in AI-based MBM interventions.

METHODS

The literature search was conducted across five major academic databases: PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. Peer-reviewed journal articles on AI-based MBM interventions with quantitative health outcomes in stress levels, heart rate variability (HRV), electroencephalography (EEG) activity, anxiety, or depression were included. Meta-analysis was performed using Cohen's d to determine effect sizes, and statistical heterogeneity was estimated using I² statistics.

RESULTS

15 studies were selected for final analysis based on inclusion and exclusion criteria. AI-based MBM interventions were found to significantly improve both mental and physiological health outcomes, such as cortisol levels, anxiety and depression scores, HRV, and EEG alpha wave activity. Although AI-assisted MBM was found to outperform the traditional approach in personalization, engagement, and adherence rate, concerns exist about data privacy, algorithmic bias, AI explainability, and user trust issues. Furthermore, AI-driven interventions showed variability in effectiveness depending on the training datasets, with a potential risk of bias in AI-generated recommendations.

CONCLUSION

AI-driven MBM interventions have immense scope in upgrading classic mind-body techniques by delivering truly personalized, adaptive, and timely interventions. However, further research and consideration are warranted to overcome ethical concerns and biases.

KEYWORDS: Artificial Intelligence, Mind-Body Medicine, Holistic Health, Biofeedback, Meditation, Yoga, Tai Chi.