Health Tech Integration: Exploring The Intersection Of Fitness Tracking And Artificial Intelligence

Purpose: While carefully weighing the related privacy and ethical concerns, this study explores how Artificial Intelligence (AI) can be integrated with health monitoring technologies, highlighting how it can improve consumer engagement, enable predictive analytics, and improve personal health insights. Methodology: Bibliometric analysis, fitness metric tracking, and predictive modelling using Random Forest algorithms and Neural Networks (Mean Absolute Error = 2.71) were all part of the mixed-methods approach that was previously used. Results: The Random Forest model identified energy expenditure, heart rate, and stress levels as the primary determinants of health outcomes. The model's ROC-AUC value (0.52), on the other hand, showed that it wasn't very good at making predictions. User responses showed that people were very worried about privacy, misuse, and bias in algorithms. Conclusion: AI shows a lot of promise for improving personalized health tracking, but it still needs big improvements in predicting accuracy, ethical safeguards, and integration into medical practice. Originality: This work uniquely integrates predictive modelling, bibliometric analysis, and a comprehensive ethical evaluation to provide a multifaceted perspective on AI-enabled health tracking.