A Machine Learning Algorithm For Developing A Smart Health System
The merging of revolutionary technologies like deep learning has greatly affected the healthcare sector, changing diagnosis and decision-making processes. This study tries to explore the role of neural networks in Digital Health research by examining 192 records drawn from the database used by Scopu...
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Published in | 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) pp. 219 - 224 |
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Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
14.05.2024
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Subjects | |
Online Access | Get full text |
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Summary: | The merging of revolutionary technologies like deep learning has greatly affected the healthcare sector, changing diagnosis and decision-making processes. This study tries to explore the role of neural networks in Digital Health research by examining 192 records drawn from the database used by Scopus. Employing a carefully made search term, the study tries to identify nations that excel in book output, primary research subjects, top funding backers, and common research terms in this domain. Notably, the debut paper on AI in smart health appeared in 2011, signalling the origins of a sizeable surge of academic output. India has emerged as the leader in performing study in this area, showing a remarkable growth trend. Furthermore, the research shows that Access from the IEEE is the top magazine, having the largest number of papers within this area. This extensive review serves as an important tool for scholars, policy makers, and healthcare workers, giving deep insights into the development of artificial intelligence in Smart Health study. The results show a positive trend, demonstrating that artificial intelligence in Connected Health is set for substantial increase in the future. The creativity and sincerity of this study highlight its importance in developing knowledge and defining the future environment of healthcare innovation. |
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DOI: | 10.1109/ICACITE60783.2024.10617243 |