Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels....

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Published inHealthcare (Basel) Vol. 11; no. 2; p. 207
Main Authors Kumar, Kamlesh, Kumar, Prince, Deb, Dipankar, Unguresan, Mihaela-Ligia, Muresan, Vlad
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 10.01.2023
MDPI
Subjects
Algorithms
Artificial intelligence
Breast cancer
Cancer therapies
Coronaviruses
COVID-19
Deep learning
Disease
Documentation
Drug dosages
Illnesses
Infrastructure
Laboratories
Ligands
Lung cancer
Machine learning
Medical imaging
Medical research
Pandemics
R&D
Research & development
Review
Robots
Skin cancer
X-rays
deep learning
artificial intelligence in healthcare
medical infrastructure
healthcare infrastructure
machine learning
artificial intelligence
healthcare and AI
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Abstract People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.
AbstractList People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.
People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.
Author Muresan, Vlad
Deb, Dipankar
Kumar, Kamlesh
Kumar, Prince
Unguresan, Mihaela-Ligia
AuthorAffiliation 3 Department of Automation, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
1 Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India
2 Department of Chemistry, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
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Keywords deep learning
artificial intelligence in healthcare
medical infrastructure
healthcare infrastructure
machine learning
artificial intelligence
healthcare and AI
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StartPage 207
SubjectTerms Algorithms
Artificial intelligence
Breast cancer
Cancer therapies
Coronaviruses
COVID-19
Deep learning
Disease
Documentation
Drug dosages
Illnesses
Infrastructure
Laboratories
Ligands
Lung cancer
Machine learning
Medical imaging
Medical research
Pandemics
R&D
Research & development
Review
Robots
Skin cancer
X-rays
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Title Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends
URI https://www.ncbi.nlm.nih.gov/pubmed/36673575
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Volume 11
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