Advances in mind-body medicine · 2026
Fusion of Artificial Intelligence and Mind-Body Medicine for Holistic Health: A Systematic Review.
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Nadaf Husain, Jabade Mangesh
Аннотация
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.
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.