Impact of AI and Dynamic Ensemble Techniques in Enhancing Healthcare Services: Opportunities and Ethical Challenges

Abstract

This review paper examines the transformative role of artificial intelligence (AI) and dynamic ensemble techniques in enhancing healthcare services. By systematically reviewing literature and case studies from the past decade, we explore how these advanced computational methods improve diagnostic accuracy, personalize treatment plans, and optimize patient monitoring. Dynamic ensemble techniques, which leverage multiple predictive models to improve outcome accuracy, offer significant promise in addressing the complexities of patient data and disease manifestations. This paper delves into applications spanning AI-driven diagnostics, personalized medicine, and remote patient monitoring, highlighting both the advancements and challenges faced in integrating these technologies into healthcare. We also address the ethical and computational challenges inherent in deploying dynamic ensemble methods and propose directions for future research. Our findings suggest that while significant progress has been made, multidisciplinary collaboration and continued innovation are crucial for overcoming current limitations and realizing the full potential of AI in healthcare.

Keywords

• Robustness
• artificial intelligence
• ensemble learning
• healthcare analytics
• dynamic ensemble techniques
• predictive analytics