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

被引：1

作者：

Javed, Haseeb ^{[1
]}

Muqeet, Hafiz Abdul ^{[2
]}

Danesh, Amirhossein ^{[3
]}

Rehman, Atiq Ur ^{[4
]}

Javed, Tahir ^{[5
]}

Bermak, Amine ^{[4
]}

机构：

[1] Muhammad Nawaz Sharif Univ Engn & Technol, Dept Elect Engn, Multan 60000, Pakistan

[2] Punjab Tianjin Univ Technol, Dept Elect Engn Technol, Lahore 54770, Pakistan

[3] Sogang Univ, Sch Engn, Dept Elect Engn, Seoul 04107, South Korea

[4] Hamad Bin Khalifa Univ, Coll Sci & Engn, Div Informat & Comp Technol, Doha, Qatar

[5] Natl Univ Comp & Emerging Sci NUCES, Dept Comp Sci, Lahore 54770, Pakistan

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Medical services; Artificial intelligence; Monitoring; Biomedical monitoring; Accuracy; Heuristic algorithms; Data models; Ensemble learning; Predictive models; Data privacy; Algorithm design and analysis; Robustness; artificial intelligence; ensemble learning; healthcare analytics; dynamic ensemble techniques; predictive analytics; computational challenges; data privacy; algorithmic adaptability; EMERGENCY-DEPARTMENT; EXPLAINABLE AI; PRIVACY; SECURE; DIAGNOSIS; SYSTEMS;

D O I：

10.1109/ACCESS.2024.3443812

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：141064 / 141087

页数：24