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Artificial Intelligence in Healthcare, Revamping the Artificial Intelligence in Medical Sector
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
AI enabled cloud based for healthcare which is able to dissect and analyze data in real-time provides knowledge on how clinical operations, patient care and health outcomes are influencing each other. This skill would consequently support decision making by health sector and the allocation of resources based on the use of clinical information, epidemiological evidence and environmental factors. Problems and benefits come with genetics information, and wearable databases, and diagnostic imaging that stores large amount of data and Electronic Health Records (EHRs). AI-enabled healthcare cloud infrastructure with complex analysis utilizing a real-time data can be an exclusive way to achieve actionable insights, and to define such insights for further patient support and decision-making. Go ahead and look towards the findings of Rajkumar et al. (2019) about the importance of real-time evaluating for clinical decision support because it allows one to conduct risk evaluation, diagnosis and therapy optimization. The application of the machine learning in analyzing the medical images, with a view to identify the diseases at an early stage and detecting them is put forward by Esteva et al. (2019).
Keywords
Healthcare Industry, Internet of Medical Things, Block chain Technology, Genomics and Precision Medicine, Artificial Intelligence.
How to cite this paper
@article{1705729,
author = {Oluwasanya Luke Ogunsakin},
title = {Artificial Intelligence in Healthcare, Revamping the Artificial Intelligence in Medical Sector},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {10},
pages = {245-258},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705729.pdf},
abstract = {AI enabled cloud based for healthcare which is able to dissect and analyze data in real-time provides knowledge on how clinical operations, patient care and health outcomes are influencing each other. This skill would consequently support decision making by health sector and the allocation of resources based on the use of clinical information, epidemiological evidence and environmental factors. Problems and benefits come with genetics information, and wearable databases, and diagnostic imaging that stores large amount of data and Electronic Health Records (EHRs). AI-enabled healthcare cloud infrastructure with complex analysis utilizing a real-time data can be an exclusive way to achieve actionable insights, and to define such insights for further patient support and decision-making. Go ahead and look towards the findings of Rajkumar et al. (2019) about the importance of real-time evaluating for clinical decision support because it allows one to conduct risk evaluation, diagnosis and therapy optimization. The application of the machine learning in analyzing the medical images, with a view to identify the diseases at an early stage and detecting them is put forward by Esteva et al. (2019).},
keywords = {Healthcare Industry, Internet of Medical Things, Block chain Technology, Genomics and Precision Medicine, Artificial Intelligence.},
month = {April},
}