Literature dissemination of artificial intelligence applications in medicine: a visualization study_Chinese Journal of Evidence-Based Medicine

Authors：

CHEN Junren ¹ , ZENG Yu ¹ , ZHANG Chao ¹ , SHEN Jiantong ² ,  YANG Xiaoyan ¹

1. West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu 610041, P.R.China;
2. Medical School of Huzhou University, Huzhou 313000, P.R.China;

Corresponding author：

YANG Xiaoyan, Email: yangxiaoyan@scu.edu.cn

Keywords：

Information visualization; Artificial intelligence; Medicine; Interdisciplinary

DOI：

10.7507/1672-2531.202102036

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To analyze publications of the application of artificial intelligence related methods in medicine.Methods PubMed and EMbase databases were electronically searched. Pathfinder Networks (PFNETs) algorithm, co-word network analysis and visualization technology were applied to analyze the time trend, journal distribution, and co-word structure of high-frequency medical keywords in key journals.Results The amount of literature published on the application of artificial intelligence related methods in the medical field had been increasing annually. Nowadays, the number of studies published in the United States was the largest, and that in China, it was the sixth (first in developing countries). The number of the first author from the United States or China were among the top two, which were significantly more than any other regions. In 2012, IEEE Trans Neural Netw Learn Syst in the computer field became one of the major contributing journals. In recent years, the methods and applications proposed in the medical field were closely related to natural language processing, neural networks, and support vector machines.Conclusions At present, the United States is in a leading position in terms of artificial intelligence in medicine, and China has also abundant research strength. The number of medical literature published in interdisciplinary journals is increasing gradually, showing that the research and application of artificial intelligence related methods in medicine have become a research hotspot in recent years.

Citation： CHEN Junren, ZENG Yu, ZHANG Chao, SHEN Jiantong, YANG Xiaoyan. Literature dissemination of artificial intelligence applications in medicine: a visualization study. Chinese Journal of Evidence-Based Medicine, 2021, 21(8): 973-979. doi: 10.7507/1672-2531.202102036 Copy

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2.	Waljee AK, Higgins PD. Machine learning in medicine: a primer for physicians. Am J Gastroenterol, 2010, 105(6): 1224-1226.
3.	Trost SG, Wong WK, Pfeiffer KA, et al. Artificial neural networks to predict activity type and energy expenditure in youth. Med Sci Sports Exerc, 2012, 44(9): 1801-1809.
4.	Kerr J, Carlson J, Godbole S, et al. Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods. Med Sci Sports Exerc, 2018, 50(7): 1518-1524.
5.	Wang G, Liu X, Li C, et al. A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images. IEEE Trans Med Imaging, 2020, 39(8): 2653-2663.
6.	Ravi D, Wong C, Deligianni F, et al. Deep Learning for Health Informatics. IEEE J Biomed Health Inform, 2017, 21(1): 4-21.
7.	Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med, 2019, 380: 1347-1358.
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10.	Shen J, Yao L, Li Y, et al. Visualization studies on evidence-based medicine domain knowledge (series 3): visualization for dissemination of evidence based medicine information. J Evid Based Med, 2011, 4(2): 96-105.
11.	Zhang Z, Wang B, Ahmed F, et al. The five Ws for information visualization with application to healthcare informatics. IEEE Trans Vis Comput Graph, 2013, 19(11): 1895-1910.
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13.	Arias-Masa J, Contreras-Vas JÁ, Izquierdo VH, et al. Measurement of the difference between students and teachers’ average networks using associative pathfinder networks. Springer: World Conference on Qualitative Research, 2019: 136-144.
14.	Amith M, Cohen T, Cunningham R, et al. Mining HPV Vaccine Knowledge Structures of Young Adults From Reddit Using Distributional Semantics and Pathfinder Networks. Cancer Control, 2020, 27(1): 1073274819891442.
15.	Blondel VD, Guillaume JL, Lambiotte R, et al. Fast unfolding of communities in large networks. J Stat Mech, 2008, 10: 10008.
16.	Newman ME. Modularity and community structure in networks. Proc Natl Acad Sci U S A, 2006, 103(23): 8577-8582.
17.	He Q. Knowledge discovery through co-word analysis. Library Trend, 1999, 48(1): 133-159.
18.	Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
19.	Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1. USA: Curran Associates Inc., 2012: 1097-1105.
20.	Deng J, Dong W, Socher R, et al. ImageNet: A large-scale hierarchical image database. Proc IEEE Comp Vis Patt Recog, 2009, 2009: 248-255.
21.	Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Springer: International Conference on Medical image computing and computer-assisted intervention, 2015: 234-241.
22.	Milletari F, Navab N, Ahmadi SA. V-Net: fully convolutional neural networks for volumetric medical image segmentation. 2016 Fourth International Conference on 3D Vision (3DV), 2016: 565-571.
23.	Qayyum A, Anwar S, Awais M, et al. Medical image retrieval using deep convolutional neural network. ArXiv, 2017, abs/1703.08472.
24.	Yamashita R, Nishio M, Do RKG, et al. Convolutional neural networks: an overview and application in radiology. Insights Imaging, 2018, 9(4): 611-629.
25.	Liu C, Gardner SJ, Wen N, et al. Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN). Int J Radiat Oncol Biol Phys, 2019, 104(4): 924-932.
26.	Hood L, Friend SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol, 2011, 8(3): 184-187.

1. Chen H, Fuller S, Friedman C, et al. Knowledge management, data mining, and text mining in medical informatics. Washington DC: Springer US, 2005.
2. Waljee AK, Higgins PD. Machine learning in medicine: a primer for physicians. Am J Gastroenterol, 2010, 105(6): 1224-1226.
3. Trost SG, Wong WK, Pfeiffer KA, et al. Artificial neural networks to predict activity type and energy expenditure in youth. Med Sci Sports Exerc, 2012, 44(9): 1801-1809.
4. Kerr J, Carlson J, Godbole S, et al. Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods. Med Sci Sports Exerc, 2018, 50(7): 1518-1524.
5. Wang G, Liu X, Li C, et al. A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images. IEEE Trans Med Imaging, 2020, 39(8): 2653-2663.
6. Ravi D, Wong C, Deligianni F, et al. Deep Learning for Health Informatics. IEEE J Biomed Health Inform, 2017, 21(1): 4-21.
7. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med, 2019, 380: 1347-1358.
8. Mane K, Mostafa J. Exploring the impact of information visualization on medical information seeking using the web. HCI International, 2005.
9. Faisal S, Blandford A, Potts HW. Making sense of personal health information: challenges for information visualization. Health Informatics J, 2013, 19(3): 198-217.
10. Shen J, Yao L, Li Y, et al. Visualization studies on evidence-based medicine domain knowledge (series 3): visualization for dissemination of evidence based medicine information. J Evid Based Med, 2011, 4(2): 96-105.
11. Zhang Z, Wang B, Ahmed F, et al. The five Ws for information visualization with application to healthcare informatics. IEEE Trans Vis Comput Graph, 2013, 19(11): 1895-1910.
12. West VL, Borland D, Hammond WE. Innovative information visualization of electronic health record data: a systematic review. J Am Med Inform Assoc, 2015, 22(2): 330-339.
13. Arias-Masa J, Contreras-Vas JÁ, Izquierdo VH, et al. Measurement of the difference between students and teachers’ average networks using associative pathfinder networks. Springer: World Conference on Qualitative Research, 2019: 136-144.
14. Amith M, Cohen T, Cunningham R, et al. Mining HPV Vaccine Knowledge Structures of Young Adults From Reddit Using Distributional Semantics and Pathfinder Networks. Cancer Control, 2020, 27(1): 1073274819891442.
15. Blondel VD, Guillaume JL, Lambiotte R, et al. Fast unfolding of communities in large networks. J Stat Mech, 2008, 10: 10008.
16. Newman ME. Modularity and community structure in networks. Proc Natl Acad Sci U S A, 2006, 103(23): 8577-8582.
17. He Q. Knowledge discovery through co-word analysis. Library Trend, 1999, 48(1): 133-159.
18. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
19. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1. USA: Curran Associates Inc., 2012: 1097-1105.
20. Deng J, Dong W, Socher R, et al. ImageNet: A large-scale hierarchical image database. Proc IEEE Comp Vis Patt Recog, 2009, 2009: 248-255.
21. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Springer: International Conference on Medical image computing and computer-assisted intervention, 2015: 234-241.
22. Milletari F, Navab N, Ahmadi SA. V-Net: fully convolutional neural networks for volumetric medical image segmentation. 2016 Fourth International Conference on 3D Vision (3DV), 2016: 565-571.
23. Qayyum A, Anwar S, Awais M, et al. Medical image retrieval using deep convolutional neural network. ArXiv, 2017, abs/1703.08472.
24. Yamashita R, Nishio M, Do RKG, et al. Convolutional neural networks: an overview and application in radiology. Insights Imaging, 2018, 9(4): 611-629.
25. Liu C, Gardner SJ, Wen N, et al. Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN). Int J Radiat Oncol Biol Phys, 2019, 104(4): 924-932.
26. Hood L, Friend SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol, 2011, 8(3): 184-187.

Chinese Journal of Evidence-Based Medicine

Literature dissemination of artificial intelligence applications in medicine: a visualization study

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