Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis

Li, Yang; Shan, Biaoan; Beiwei, Li; Liu, Xiaoju; Pu, Yi

doi:10.1155/2021/9739219

Cited by 33 publications

(24 citation statements)

References 39 publications

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“…Review and explain the broad solutions to the statistical challenges, system challenges, and privacy concerns inherent in federated learning, while emphasizing the implications and potential for healthcare. Li et al [29] offered an overview of the implementation of ML and bioinformatics technologies in the medical business utilizing bibliometric visualization and Web of Science (WOS). A review focuses on the countries that conduct the most research, the primary research subjects, funding sources, and hotspots for research in this field.…”

Section: Literature Reviewmentioning

confidence: 99%

A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique

Rehman

Abbas

Khan

et al. 2022

Computers in Biology and Medicine

103

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Section: Literature Reviewmentioning

confidence: 99%

A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique

Rehman

Abbas

Khan

et al. 2022

Computers in Biology and Medicine

103

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“…The applications are robotics, machine learning (ML), reasoning and decision-making, natural language processing, and computer vision. 7 AI improves the relationship between patients and medical care by facilitating personalized treatment and medical practice from a traditional perspective. AI enhances the accuracy, speed, and quality of the analysis and treatment of patients.…”

Section: Introductionmentioning

confidence: 99%

Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector

et al. 2023

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Artificial Intelligent (AI) applications in e-health have evolved considerably in the last 25 years. To track the current research progress in this field, there is a need to analyze the most recent trend of adopting AI applications in e-health. This bibliometric analysis study covers AI applications in e-health. It differs from the existing literature review as the journal articles are obtained from the Scopus database from its beginning to late 2021 (25 years), which depicts the most recent trend of AI in e-health. The bibliometric analysis is employed to find the statistical and quantitative analysis of available literature of a specific field of study for a particular period. An extensive global literature review is performed to identify the significant research area, authors, or their relationship through published articles. It also provides the researchers with an overview of the work evolution of specific research fields. The study's main contribution highlights the essential authors, journals, institutes, keywords, and states in developing the AI field in e-health.

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“…Identification studies using radio diagnostic imaging for body and skeletal remains and metric measurement methods for osseous structures are one of the most interesting new methods in this field [4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Classification is a machine learning method used to learn how to assign a class label to an input sample [14]. Each classification algorithm has its strengths and weaknesses.…”

Section: Introductionmentioning

confidence: 99%

Determining the Age Range Based on Machine-Learning Methods From Facial Skeletal Angles (Glabella and Maxilla Angle and Length and Width of Piriformis) in CT Scan

Mohtarami

Hedjazi²,

Manouchehri³

2022

IJMTFM

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Background: One of the main steps in identifying a person in forensic medicine is determining the age of skeletal remains, including the skull. This study aimed to investigate the possibility of predicting age from facial angles (glabella, piriformis, and maxillary angle and measuring peripheral length and width) with artificial intelligence in a CT scan. Methods: The cross-sectional study method is simple random sampling using a questionnaire. Accurately measurable CT scan samples are selected. For exclusion criteria, gender uncertainty, and the possibility of measurement based on CT scan quality, the researchers examined the facial angles (angle of the glabella and maxilla and length and width of the piriformis) for 100 men and 100 women. The Mean±SD of the age was 39.16±2.22 years for men and 47.84±2.46 years for women. The samples were classified based on age differences, and then the data were analyzed using machine learning algorithms to determine the age group. Results: After determining the exact amount of measurement, the data were evaluated by machine learning algorithms to determine the age group. Accordingly, in the age group classification based on the World Health Organization (WHO) (with an age difference of 10 years) (years±5) with 100% accuracy and in the second classification (with an age difference of 5 years) (years±2.5) with 88% accuracy and 79% precision of the age group was predicted. Conclusion: The obtained data show the importance of new artificial intelligence methods, including machine learning, in providing new methods to determine age groups (age±2.5) through skull angles with high accuracy in cases where even cranial remains are found in identification in forensic medicine.

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Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis

Cited by 33 publications

References 39 publications

A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique

A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique

Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector

Determining the Age Range Based on Machine-Learning Methods From Facial Skeletal Angles (Glabella and Maxilla Angle and Length and Width of Piriformis) in CT Scan

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