An Integration of Blockchain and Machine Learning into the Health Care System: A Meta-Analysis Approach

Blockchain integrated with Machine Learning (ML) has recently attracted the attention of academia and industries in the health care system because it improves security, predictive features, and operational work. However, the potential of having all these technologies combined is most often rather underestimated. This article offers a systematic review and an up-to-date literature review to provide a meta-analysis of the current literature on the integration of blockchain and machine learning toward the purpose of identifying their impact on addressing significant problems in the field of health care including confidentiality for patients, integration, and elaborate diagnosing. A total of 35 studies were considered in articles with applications covered by staking sharing, prediction and analysis, and pharmacogenomic individualized therapy. Combined blockchain and ML applications in health care demonstrated substantial advantages through the published meta-analysis. The pooled effect size was 17.85 (95% CI: 10). As a measure of improvement, the meta-analysis found the combination of blockchain with ML raises health care system data protection alongside diagnostic reliability and operational excellence (95% CI: 80 to 24.89). The many variations found across studies generated excessive heterogeneity as demonstrated bymjI2 equaling 100% and Tau2 equaling 451.94. These results confirm the strong transformational potentials of these technological solutions. Future research projects should establish the feasibility of adding blockchain along with machine learning systems because the appointed answers show their potential to improve health care functionality.