Abstract
Health care is one of the most important agenda in every nation’s economic and social growth. Due to amalgamation in food habits and lifestyle, today’s health care is under tremendous burden because of an increase in chronic diseases. An era of health care is under development due to the fast and accurate diagnosis ability of artificial intelligence from the last 2 years. Due to AI, it is possible in the future to provide cost-effective models in health care at the reach of every patient. The basic motivation behind this paper is to insight some of the AI models of the health care, their future scope, and implementation problems. This study will surely help to identify the thought process behind each model and generate some new guidelines for implementation in the future. These models require certain modifications in terms of data handling, security, image enhancement, and reconstruction. Use of smartphone and wearable devices has increased rapidly, and hence we need to provide a unique solution to society in terms of the new techniques of artificial intelligence.
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References
B.J. Lee, J.Y. Kim, Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE J. Biomed. Health Inform. 20(1) (2016)
J. Thevenot, M.B. Lopez, A. Hadid, A survey on computer vision for assistive medical diagnosis from faces. IEEE J. Biomed. Health Inform. 22(5), 1497–1511 (2018). https://doi.org/10.1109/JBHI.2017.2754861
S. Ram, W. Zhang, M. Williams, Y. Pengetnze, Predicting asthma-related emergency department visits using Big Data. IEEE J. Biomed. Health Inform. 19(4), 1216–1223 (2015). https://doi.org/10.1109/JBHI.2015.2404829
J.H. Brenas, M.S. Al-Manir, C.J.O. Baker, A. Shaban-Nejad, A malaria analytics framework to support evolution and interoperability of global health surveillance systems. IEEE Access 5, 21605–21619 (2017)
O. Lahdenoja, T. Hurnanen, Z. Iftikhar, S. Nieminen, T. Knuutila, A. Saraste, T. Koivisto et al., Atrial fibrillation detection via accelerometer and gyroscope of a smartphone. IEEE J. Biomed. Health Inform. 22(1), 108–118 (2018). https://doi.org/10.1109/JBHI.2017.2688473
W.D. Kearns, J.L. Fozard, V.O. Nams, Movement path tortuosity in free ambulation: relationships to age and brain disease. IEEE J. Biomed. Health Inform. 21(2), 539–548 (2017). https://doi.org/10.1109/JBHI.2016.2517332
K. Seol, Y.G. Kim, E. Lee, Y.D. Seo, D.K. Baik, Privacy-preserving attribute-based access control model for the XML-based electronic health record system. IEEE Access 6, 9114–9128 (2018). https://doi.org/10.1109/ACCESS.2018.2800288
L. Yu, W.M. Chan, Y. Zhao, K.L. Tsui, The personalized health monitoring system of elderly wellness at the community level in Hong Kong. IEEE Access 6, 35558–35567 (2018). https://doi.org/10.1109/ACCESS.2018.2848936
R. Sanchez-Guerrero, F.A. Mendoza, D. Diaz-Sanchez, P.A. Cabarcos, A.M. Lopez, Collaborative eHealth meets security: privacy-enhancing patient profile management. IEEE J. Biomed. Health Inform. 21(6), 1741–1749 (2017). https://doi.org/10.1109/JBHI.2017.2655419
M.M. Anthimopoulos, L. Gianola, L. Scarnato, P. Diem, S.G. Mougiakakou, A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE J. Biomed. Health Inform. 18(4), 1261–1271 (2014). https://doi.org/10.1109/JBHI.2014.2308928
A.A. Rizvi, Nutritional challenges in the elderly with diabetes. Int. J. Diabetes Mellit. (2009). https://doi.org/10.1016/j.ijdm.2009.05.002
H.C. Wang, A.R. Lee, Recent developments in blood glucose sensors. J. Food Drug Anal. (2015). Elsevier Taiwan LLC. https://doi.org/10.1016/j.jfda.2014.12.001
D. Ravi, C. Wong, B. Lo, G.Z. Yang, A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE J. Biomed. Health Inform. 21(1), 56–64 (2017). https://doi.org/10.1109/JBHI.2016.2633287
D. Stern, C. Payer, N. Giuliani, M. Urschler, Automatic age estimation and majority age classification from multi-factorial MRI data. IEEE J. Biomed. HealthInformatics (2018). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/JBHI.2018.2869606
E. Shelhamer, J. Long, T. Darrell, Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017). https://doi.org/10.1109/TPAMI.2016.2572683
W.Y. Lin, W.C. Chou, P.C. Chang, C.C. Chou, M.S. Wen, M.Y. Ho, M.Y. Lee et al., Identification of location specific feature points in a cardiac cycle using a novel seismocardiogram spectrum system. IEEE J. Biomed. Health Inform. 22(2), 442–449 (2018)
H.N. Bharath, D.M. Sima, N. Sauwen, U. Himmelreich, L. De Lathauwer, S. Van Huffel, Nonnegative canonical polyadic decomposition for tissue-type differentiation in gliomas. IEEE J. Biomed. Health Inform. 21(4), 1124–1132 (2017). https://doi.org/10.1109/JBHI.2016.2583539
S.G. Kandlikar, I. Perez-Raya, P.A. Raghupathi, J.L. Gonzalez-Hernandez, D. Dabydeen, L. Medeiros, P. Phatak, Infrared imaging technology for breast cancer detection—current status, protocols, and new directions. Int. J. Heat Mass Transf. (2017). Elsevier Ltd. https://doi.org/10.1016/j.ijheatmasstransfer.2017.01.086
R. Miotto, F. Wang, S. Wang, X. Jiang, J.T. Dudley, Deep learning for healthcare: review, opportunities, and challenges. Brief. Bioinform. (2017). https://doi.org/10.1093/bib/bbx044
M. Pavel, H.B. Jimison, H.D. Wactlar, T.L. Hayes, W. Barkis, J. Skapik, J. Kaye, The role of technology and engineering models in transforming healthcare. IEEE Rev. Biomed. Eng. 6, 156–177 (2013). https://doi.org/10.1109/RBME.2012.2222636
F. Pesapane, M. Codari, F. Sardanelli, Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur. Radiol. Exp. 2(1), 35 (2018). https://doi.org/10.1186/s41747-018-0061-6
C. Liew, The future of radiology augmented with artificial intelligence: a strategy for success. Eur. J. Radiol. (2018). Elsevier Ireland Ltd. https://doi.org/10.1016/j.ejrad.2018.03.019
M. De La Puente-Yagüe, M.A. Cuadrado-Cenzual, M.J. Ciudad-Cabañas, M. Hernández-Cabria, L. Collado-Yurrita, Vitamin D: and its role in breast cancer. Kaohsiung J. Med. Sci. (2018). Elsevier (Singapore) Pte Ltd. https://doi.org/10.1016/j.kjms.2018.03.004
F. Pan, P. He, C. Liu, T. Li, A. Murray, D. Zheng, Variation of the Korotkoff stethoscope sounds during blood pressure measurement: Analysis using a convolutional neural network. IEEE J. Biomed. Health Inform. 21(6), 1593–1598 (2017). https://doi.org/10.1109/JBHI.2017.2703115
M.H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, R. Martí et al., Automated Breast ultrasound lesions detection using convolutional neural networks. IEEE J. Biomed. Health Inform. 22(4), 1218–1226 (2018). https://doi.org/10.1109/JBHI.2017.2731873
O. Oktay, W. Bai, R. Guerrero, M. Rajchl, A. De Marvao, D.P. O’Regan, D. Rueckert et al., Stratified decision forests for accurate anatomical landmark localization in cardiac images. IEEE Trans. Med. Imaging 36(1), 332–342 (2017). https://doi.org/10.1109/TMI.2016.2597270
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Sharma, K.K., Pawar, S.D., Bali, B. (2020). Proactive Preventive and Evidence-Based Artificial Intelligene Models: Future Healthcare. In: Singh Tomar, G., Chaudhari, N.S., Barbosa, J.L.V., Aghwariya, M.K. (eds) International Conference on Intelligent Computing and Smart Communication 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0633-8_44
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