scholarly journals Artificial Intelligence in Health Care: Bibliometric Analysis (Preprint)

2020 ◽  
Author(s):  
Yuqi Guo ◽  
Zhichao Hao ◽  
Shichong Zhao ◽  
Jiaqi Gong ◽  
Fan Yang
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Health Care ◽  
Growth Rate ◽  
Bibliometric Analysis ◽  
Convolutional Neural Networks ◽  
Health Care Research ◽  
Support Vector ◽  
Research Papers ◽  
Care Research

BACKGROUND As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. OBJECTIVE The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. METHODS The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. RESULTS The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. CONCLUSIONS This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

scholarly journals Artificial Intelligence in Health Care: Bibliometric Analysis

10.2196/18228 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e18228 ◽  
Author(s):  
Yuqi Guo ◽  
Zhichao Hao ◽  
Shichong Zhao ◽  
Jiaqi Gong ◽  
Fan Yang
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Health Care ◽  
Growth Rate ◽  
Bibliometric Analysis ◽  
Convolutional Neural Networks ◽  
Health Care Research ◽  
Support Vector ◽  
Research Papers ◽  
Care Research

Background As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. Methods The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. Results The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. Conclusions This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

scholarly journals Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta-analysis

2021 ◽  
Author(s):  
Al-Rahim Habib ◽  
Majid Kajbafzadeh ◽  
Zubair Hasan ◽  
Eugene Wong ◽  
Hasantha Gunasekera ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Otitis Media ◽  
Convolutional Neural Networks ◽  
Otitis Media With Effusion ◽  
Meta Analysis ◽  
Acute Otitis Media ◽  
Chronic Otitis Media ◽  
Ground Truth ◽  
Support Vector

Objective: To summarize the accuracy of artificial intelligence (AI) computer vision algorithms to classify ear disease from otoscopy. Methods: Using the PRISMA guidelines, nine online databases were searched for articles that used AI methods (convolutional neural networks, artificial neural networks, support vector machines, decision trees, k-nearest neighbors) to classify otoscopic images. Diagnostic classes of interest: normal tympanic membrane, acute otitis media (AOM), otitis media with effusion (OME), chronic otitis media (COM) with or without perforation, cholesteatoma, and canal obstruction. Main Outcome Measures: Accuracy to correctly classify otoscopic images compared to otolaryngologists (ground-truth). The Quality Assessment of Diagnostic Accuracy Studies Version 2 tool was used to assess the quality of methodology and risk of bias. Results: Thirty-nine articles were included. Algorithms achieved 90.7% (95%CI: 90.1 – 91.3%) accuracy to difference between normal or abnormal otoscopy images in 14 studies. The most common multi-classification algorithm (3 or more diagnostic classes) achieved 97.6% (95%CI: 97.3.- 97.9%) accuracy to differentiate between normal, AOM and OME in 3 studies. Compared to manual classification, AI algorithms outperformed human assessors to classify otoscopy images achieving 93.4% (95%CI: 90.5 – 96.4%) versus 73.2% (95%CI: 67.9 – 78.5%) accuracy in 3 studies. Convolutional neural networks achieved the highest accuracy compared to other classification methods. Conclusion: AI can classify ear disease from otoscopy. A concerted effort is required to establish a comprehensive and reliable otoscopy database for algorithm training. An AI-supported otoscopy system may assist health care workers, trainees, and primary care practitioners with less otology experience identify ear disease.

Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Alexander Knyshov ◽  
Samantha Hoang ◽  
Christiane Weirauch
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Training Image ◽  
Species Level ◽  
Identification Accuracy ◽  
Support Vector ◽  
Test Case ◽  
Learning Approaches ◽  
Lower Accuracy ◽  
Neural Network Classifiers

Abstract Automated insect identification systems have been explored for more than two decades but have only recently started to take advantage of powerful and versatile convolutional neural networks (CNNs). While typical CNN applications still require large training image datasets with hundreds of images per taxon, pretrained CNNs recently have been shown to be highly accurate, while being trained on much smaller datasets. We here evaluate the performance of CNN-based machine learning approaches in identifying three curated species-level dorsal habitus datasets for Miridae, the plant bugs. Miridae are of economic importance, but species-level identifications are challenging and typically rely on information other than dorsal habitus (e.g., host plants, locality, genitalic structures). Each dataset contained 2–6 species and 126–246 images in total, with a mean of only 32 images per species for the most difficult dataset. We find that closely related species of plant bugs can be identified with 80–90% accuracy based on their dorsal habitus alone. The pretrained CNN performed 10–20% better than a taxon expert who had access to the same dorsal habitus images. We find that feature extraction protocols (selection and combination of blocks of CNN layers) impact identification accuracy much more than the classifying mechanism (support vector machine and deep neural network classifiers). While our network has much lower accuracy on photographs of live insects (62%), overall results confirm that a pretrained CNN can be straightforwardly adapted to collection-based images for a new taxonomic group and successfully extract relevant features to classify insect species.

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