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The Chinese Journal of Artificial Intelligence

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ISSN (Print): 2666-7827
ISSN (Online): 2666-7835

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Mini-Review Article

Potential Use of Artificial Intelligence in a Healthcare System

Author(s): Janvi S. Madhavi and Ojaskumar D. Agrawal*

Volume 1, Issue 2, 2022

Published on: 06 September, 2022

Article ID: e050822207306 Pages: 12

DOI: 10.2174/2666782701666220805105655

Price: $65

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Abstract

Artificial Intelligence (AI) is a swiftly evolving branch of technology that has been used to improve clinical practice, minimize errors, and boost safety and efficiency worldwide; in almost every field. AI is used for machine-learning algorithms and techniques to replicate human cognition in the assessment, display, and interpretation of complicated medical and healthcare data. AI is surfacing and producing a discernible shift in the healthcare system by expanding the availability of data in healthcare and speeding up the development of analysis tools. Additionally, AI and its applications in healthcare have evolved and proved to be a boon. The pharmaceutical business, health services, medical institutes, and patients, not only doctors use the applications but also dermatology, echocardiography, surgery, and angiography are only a few applications. AI can improve healthcare systems without hesitation. Automating time-consuming tasks can free up clinicians' schedules so they can encounter patients. It is causing a radical shift in healthcare, attributed to the increasing availability of healthcare data and the rapid advancement of advanced analytics. Screening, monitoring, and medical and clinical investigations are all made easier by AI. Despite some of the obstacles and limitations that AI faces, this new technology has enormous potential in the medical field. Regarding their reduced size, electronic devices have become more powerful as technology has progressed. Currently, the COVID – 19 pandemic is propelling the digital age to unprecedented heights. On multiple fronts, Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) are being employed to combat the pandemic.

Keywords: Artificial Intelligence, Healthcare, Robotics, Machine Learning, Deep Learning, Robot Nurses, Patients, Device

Graphical Abstract

[1]
Rong, G.; Mendez, A.; Bou Assi, E.; Zhao, B.; Sawan, M. Artificial intelligence in healthcare: Review and prediction case studies. Engi-neering, 2020, 6(3), 291-301.
[http://dx.doi.org/10.1016/j.eng.2019.08.015]
[2]
LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature, 2015, 521(7553), 436-444.
[http://dx.doi.org/10.1038/nature14539] [PMID: 26017442]
[3]
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; , 2016; pp. 770-778.
[http://dx.doi.org/10.1109/CVPR.2016.90]
[4]
Ng, J.Y.H.; Hausknecht, M.; Vijayanarasimhan, S.; Vinyals, O.; Monga, R.; Toderici, G. Beyond short snippets: Deep networks for video classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition; , 2015; pp. 4694-4702.
[http://dx.doi.org/10.1109/CVPR.2015.7299101]
[5]
Jiang, W.; Zhang, L. Geospatial data to images: A deep-learning framework for traffic forecasting. Tsinghua Sci. Technol., 2019, 24(1), 52-64.
[http://dx.doi.org/10.26599/TST.2018.9010033]
[6]
Deo, R.C. Machine learning in medicine. Circulation, 2015, 132(20), 1920-1930.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.115.001593] [PMID: 26572668]
[7]
Ljosa, V.; Sokolnicki, K.L.; Carpenter, A.E. Annotated high-throughput microscopy image sets for validation. Nat. Methods, 2012, 9(7), 637.
[http://dx.doi.org/10.1038/nmeth.2083] [PMID: 22743765]
[8]
Darcy, A.M.; Louie, A.K.; Roberts, L.W. Machine learning and the profession of medicine. JAMA, 2016, 315(6), 551-552.
[http://dx.doi.org/10.1001/jama.2015.18421] [PMID: 26864406]
[9]
James, G.; Witten, D.; Hastie, T.; Tibshirani, R. An introduction to statistical learning In: Springer Nature: NY; , 2013; 103, pp. 1-14.
[http://dx.doi.org/10.1007/978-1-4614-7138-7]
[10]
Wang, Y.; Tetko, I.V.; Hall, M.A.; Frank, E.; Facius, A.; Mayer, K.F.X.; Mewes, H.W. Gene selection from microarray data for cancer classification--a machine learning approach. Comput. Biol. Chem., 2005, 29(1), 37-46.
[http://dx.doi.org/10.1016/j.compbiolchem.2004.11.001] [PMID: 15680584]
[11]
Murdoch, T.B.; Detsky, A.S. The inevitable application of big data to health care. JAMA, 2013, 309(13), 1351-1352.
[http://dx.doi.org/10.1001/jama.2013.393] [PMID: 23549579]
[12]
Lee, C.S.; Nagy, P. G.; Weaver, S. J.; Newman-Toker, D. E. Cognitive AJR, and system factors contributing to diagnostic errors in radiology., 2013, 201(3), 611-617.
[http://dx.doi.org/10.2214/AJR.12.10375] [PMID: 23971454]
[13]
Esteva, A.; Kuprel, B.; Novoa, R.A.; Ko, J.; Swetter, S.M.; Blau, H.M.; Thrun, S. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639), 115-118.
[http://dx.doi.org/10.1038/nature21056] [PMID: 28117445]
[14]
Heaton, J. Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning. Genet. Program. Evolvable Mach., 2018, 19, 305-307. [https://doi.org/10.1007/s10710-017-9314-z]
[15]
Somashekhar, S.; Kumarc, R.; Rauthan, A.; Arun, K.; Patil, P.; Ramya, Y. Abstract S6-07: Double blinded validation study to assess performance of IBM artificial intelligence platform, watson for oncology in comparison with manipal multidisciplinary tumour board – first study of 638 breast cancer cases. Cancer Res., 2017, 77(4), S6-S07.
[http://dx.doi.org/10.1158/1538-7445.SABCS16-S6-07]
[16]
Bouton, C.E.; Shaikhouni, A.; Annetta, N.V.; Bockbrader, M.A.; Friedenberg, D.A.; Nielson, D.M.; Sharma, G.; Sederberg, P.B.; Glenn, B.C.; Mysiw, W.J.; Morgan, A.G.; Deogaonkar, M.; Rezai, A.R. Restoring cortical control of functional movement in a human with quad-riplegia. Nature, 2016, 533(7602), 247-250.
[http://dx.doi.org/10.1038/nature17435] [PMID: 27074513]
[17]
Farina, D.; Vujaklija, I.; Sartori, M.; Kapelner, T.; Negro, F.; Jiang, N.; Bergmeister, K.; Andalib, A.; Principe, J.; Aszmann, O.C. Man/Machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation. Nat. Biomed. Eng., 2017, 1, 0025.
[18]
First FDA approval for clinical cloud-based deep learning in healthcare. Available from: https://www.forbes.com/sites/bernardmarr/2017/01/20/first-fda-approval-for-clinical-cloud-based-deep-learning-in-healthcare/?sh=21de6c41161c
[19]
Long, E.; Lin, H.; Liu, Z.; Wu, X.; Wang, L.; Jiang, J.; An, Y.; Lin, Z.; Li, X.; Chen, J.; Li, J.; Cao, Q.; Wang, D.; Liu, X.; Chen, W.; Liu, Y. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat. Biomed. Eng., 2017, 1, 0024.
[20]
Gulshan, V.; Peng, L.; Coram, M.; Stumpe, M.C.; Wu, D.; Narayanaswamy, A.; Venugopalan, S.; Widner, K.; Madams, T.; Cuadros, J.; Kim, R.; Raman, R.; Nelson, P.C.; Mega, J.L.; Webster, D.R. Development and validation of a deep learning algorithm for detection of di-abetic retinopathy in retinal fundus photographs. JAMA, 2016, 316(22), 2402-2410.
[http://dx.doi.org/10.1001/jama.2016.17216] [PMID: 27898976]
[21]
Saenger, A.K.; Christenson, R.H. Stroke biomarkers: Progress and challenges for diagnosis, prognosis, differentiation, and treatment. Clin. Chem., 2010, 56(1), 21-33.
[http://dx.doi.org/10.1373/clinchem.2009.133801] [PMID: 19926776]
[22]
Heeley, E.; Anderson, C.S.; Huang, Y.; Jan, S.; Li, Y.; Liu, M.; Sun, J.; Xu, E.; Wu, Y.; Yang, Q.; Zhang, J.; Zhang, S.; Wang, J. Role of health insurance in averting economic hardship in families after acute stroke in China. Stroke, 2009, 40(6), 2149-2156.
[http://dx.doi.org/10.1161/STROKEAHA.108.540054] [PMID: 19359646]
[23]
Zhang, Q.; Xie, Y.; Ye, P.; Pang, C. Acute ischaemic stroke prediction from physiological time series patterns. Australas. Med. J., 2013, 6(5), 280-286.
[http://dx.doi.org/10.4066/AMJ.2013.1650] [PMID: 23745149]
[24]
Villar, J.R.; González, S.; Sedano, J.; Chira, C.; Trejo-Gabriel-Galan, J.M. Improving human activity recognition and its application in early stroke diagnosis. Int. J. Neural Syst., 2015, 25(4), 1450036.
[http://dx.doi.org/10.1142/S0129065714500361] [PMID: 25684369]
[25]
Grefkes, C.; Fink, G.R. Recovery from stroke: Current concepts and future perspectives. Neurol. Res. Pract., 2020, 2(1), 1-10.
[http://dx.doi.org/10.1186/s42466-020-00060-6]
[26]
Mannini, A.; Trojaniello, D.; Cereatti, A.; Sabatini, A.M. A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington’s disease patients. Sensors, 2016, 16(1), 134.
[http://dx.doi.org/10.3390/s16010134] [PMID: 26805847]
[27]
Thornhill, R.E.; Lum, C.; Jaberi, A.; Stefanski, P.; Torres, C.H.; Momoli, F.; Petrcich, W.; Dowlatshahi, D. Can shape analysis differenti-ate free-floating internal carotid artery thrombus from atherosclerotic plaque in patients evaluated with CTA for stroke or transient is-chemic attack? Acad. Radiol., 2014, 21(3), 345-354.
[http://dx.doi.org/10.1016/j.acra.2013.11.011] [PMID: 24507422]
[28]
Rehme, A.K.; Volz, L.J.; Feis, D.L.; Bomilcar-Focke, I.; Liebig, T.; Eickhoff, S.B.; Fink, G.R.; Grefkes, C. Identifying neuroimaging markers of motor disability in acute stroke by machine learning techniques. Cereb. Cortex, 2015, 25(9), 3046-3056.
[http://dx.doi.org/10.1093/cercor/bhu100] [PMID: 24836690]
[29]
Griffis, J.C.; Allendorfer, J.B.; Szaflarski, J.P.; Griffis, J.C.; Allendorfer, J.B.; Szaflarski, J.P. Voxel-based gaussian naïve bayes classifi-cation of ischemic stroke lesions in individual T1-weighted MRI scans. J. Neurosci. Methods, 2016, 257, 97-108.
[http://dx.doi.org/10.1016/j.jneumeth.2015.09.019]
[30]
Kamnitsas, K.; Ledig, C.; Newcombe, V.F.J.; Simpson, J.P.; Kane, A.D.; Menon, D.K.; Rueckert, D.; Glocker, B. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal., 2017, 36, 61-78.
[http://dx.doi.org/10.1016/j.media.2016.10.004] [PMID: 27865153]
[31]
Rondina, J.M.; Filippone, M.; Girolami, M.; Ward, N.S. Decoding post-stroke motor function from structural brain imaging. Neuroimage Clin., 2016, 12, 372-380.
[http://dx.doi.org/10.1016/j.nicl.2016.07.014] [PMID: 27595065]
[32]
Jiang, F.; Jiang, Y.; Zhi, H.; Dong, Y.; Li, H.; Ma, S.; Wang, Y.; Dong, Q.; Shen, H.; Wang, Y. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc. Neurol., 2017, 2(4), 230-243.
[http://dx.doi.org/10.1136/svn-2017-000101] [PMID: 29507784]
[33]
Yu, K.H.; Beam, A.L.; Kohane, I.S. Artificial intelligence in healthcare. Nat. Biomed. Eng., 2018, 2(10), 719-731.
[http://dx.doi.org/10.1038/s41551-018-0305-z] [PMID: 31015651]
[34]
Ledford, J.R. Chest radiology: Plain film patterns and differential diagnoses, 6th; James, C. Reed., Ed.; Philadelphia, PA: Elsevier, 2011, 197, p. 455.
[http://dx.doi.org/10.2214/AJR.11.7214]
[35]
Rigel, D.S.; Friedman, R.J.; Kopf, A.W.; Polsky, D. ABCDE--an evolving concept in the early detection of melanoma. Arch. Dermatol., 2005, 141(8), 1032-1034.
[http://dx.doi.org/10.1001/archderm.141.8.1032] [PMID: 16103334]
[36]
Thomas, L.; Tranchand, P.; Berard, F.; Secchi, T.; Colin, C.; Moulin, G. Semiological value of ABCDE criteria in the diagnosis of cutane-ous pigmented tumors. Dermatology, 1998, 197(1), 11-17.
[http://dx.doi.org/10.1159/000017969] [PMID: 9693179]
[37]
Zheng, Y.; He, M.; Congdon, N. Worldwide epidemic of diabetic retinopathy. Indian J. Ophthalmol., 2012, 60(5), 428-431.
[http://dx.doi.org/10.4103/0301-4738.100542] [PMID: 22944754]
[38]
Menke, A.; Casagrande, S.; Geiss, L.; Cowie, C.C. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. JAMA, 2015, 314(10), 1021-1029.
[http://dx.doi.org/10.1001/jama.2015.10029] [PMID: 26348752]
[39]
Rubegni, P.; Cevenini, G.; Burroni, M.; Perotti, R.; Dell’Eva, G.; Sbano, P.; Miracco, C.; Luzi, P.; Tosi, P.; Barbini, P.; Andreassi, L. Au-tomated diagnosis of pigmented skin lesions. Int. J. Cancer, 2002, 101(6), 576-580.
[http://dx.doi.org/10.1002/ijc.10620] [PMID: 12237900]
[40]
Stang, A.; Pohlabeln, H.; Müller, K.M.; Jahn, I.; Giersiepen, K.; Jöckel, K.H. Diagnostic agreement in the histopathological evaluation of lung cancer tissue in a population-based case-control study. Lung Cancer, 2006, 52(1), 29-36.
[http://dx.doi.org/10.1016/j.lungcan.2005.11.012] [PMID: 16476504]
[41]
Quang, D.; Chen, Y.; Xie, X. DANN: A deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics, 2015, 31(5), 761-763.
[http://dx.doi.org/10.1093/bioinformatics/btu703] [PMID: 25338716]
[42]
Quang, D.; Xie, X.; Dan, Q.; Dan, Q. A hybrid convolutional and recurrent deep neural network for quantifying the function of DNA se-quences. Nucleic Acids Res., 2016, 44(11), e107.
[http://dx.doi.org/10.1093/nar/gkw226] [PMID: 27084946]
[43]
Saria, S.A. $3 Trillion challenge to computational scientists: Transforming healthcare delivery. IEEE Intell. Syst., 2014, 29(4), 82-87.
[http://dx.doi.org/10.1109/MIS.2014.58]
[44]
Ramesh, A.N.; Kambhampati, C.; Monson, J.R.T.; Drew, P.J. Artificial intelligence in medicine. Ann. R. Coll. Surg. Engl., 2004, 86(5), 334-338.
[http://dx.doi.org/10.1308/147870804290] [PMID: 15333167]
[45]
Hamet, P.; Tremblay, J. Artificial intelligence in medicine. Metabolism, 2017, 69S, S36-S40.
[http://dx.doi.org/10.1016/j.metabol.2017.01.011] [PMID: 28126242]
[46]
Shah, A.; Ahirrao, S.; Phansalkar, S.; Kotecha, K. Survey on: Applications of smart wearable technology in health insurance. IOP Conf. Ser. Mater. Sci. Eng., 2021, 1042(1), p. 12025.
[http://dx.doi.org/10.1088/1757-899X/1042/1/012025]
[47]
Hassan, M.M.; Huda, S.; Uddin, M.Z.; Almogren, A.; Alrubaian, M. Human activity recognition from body sensor data using deep learn-ing. J. Med. Syst., 2018, 42(6), 99.
[http://dx.doi.org/10.1007/s10916-018-0948-z] [PMID: 29663090]
[48]
Uddin, M.Z.; Soylu, A. Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning. Sci. Rep., 2021, 11(1), 16455.
[http://dx.doi.org/10.1038/s41598-021-95947-y] [PMID: 34385552]
[49]
Capobianco, G.; Di Giacomo, U.; Martinelli, F.; Mercaldo, F.; Santone, A. "Wearable devices for human activity recognition and user detection," 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) 2019, pp. 365-372.
[http://dx.doi.org/10.1109/EMPDP.2019.8671636]
[50]
Guo, X.; Liu, L.; Zhang, Z.; Chun, Yu J. A review of AI technologies for wearable devices. IOP Conf. Ser. Mater. Sci. Eng., 2019, 688(4), p. 044072.
[http://dx.doi.org/10.1088/1757-899X/688/4/044072]
[51]
Axisa, F.; Schmitt, P.M.; Gehin, C.; Delhomme, G.; McAdams, E.; Dittmar, A. Flexible technologies and smart clothing for citizen medi-cine, home healthcare, and disease prevention. IEEE Trans. Inf. Technol. Biomed., 2005, 9(3), 325-336.
[http://dx.doi.org/10.1109/TITB.2005.854505] [PMID: 16167686]
[52]
Kubota, K.J.; Chen, J.A.; Little, M.A. Machine learning for large-scale wearable sensor data in Parkinson’s disease: Concepts, promises, pitfalls, and futures. Mov. Disord., 2016, 31(9), 1314-1326.
[http://dx.doi.org/10.1002/mds.26693] [PMID: 27501026]
[53]
Guo, J.; Li, B. The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity, 2018, 2(1), 174-181.
[http://dx.doi.org/10.1089/heq.2018.0037] [PMID: 30283865]
[54]
Khan, Z.H.; Siddique, A.; Lee, C.W. Robotics utilization for healthcare digitization in global COVID-19 management. Int. J. Environ. Res. Public Health, 2020, 17(11), 3819.
[http://dx.doi.org/10.3390/ijerph17113819] [PMID: 32481547]
[55]
Locsin, R.C.; Ito, H. Can humanoid nurse robots replace human nurses? J. Nurs., 2018, 5(1), 1.
[http://dx.doi.org/10.7243/2056-9157-5-1]
[56]
Bai, L.; Yang, J.; Chen, X.; Sun, Y.; Li, X. Medical robotics in bone fracture reduction surgery: A review. Sensors, 2019, 19(16), 3593.
[http://dx.doi.org/10.3390/s19163593] [PMID: 31426577]
[57]
Balasubramanian, S.; Chenniah, J.; Balasubramanian, G.; Vellaipandi, V. The era of robotics: Dexterity for surgery and medical care: Narrative review. Int. Surg. J., 2020, 7(4), 1317-1323.
[http://dx.doi.org/10.18203/2349-2902.isj20201057]
[58]
Locsin, R.C.; Ito, H.; Tanioka, T.; Yasuhara, Y.; Osaka, K.; Schoenhofer, S.O. Humanoid nurse robots as caring entities: A revolutionary probability? Int. J. Stud. Nurs., 2018, 3(2), 146.
[http://dx.doi.org/10.20849/ijsn.v3i2.456]
[59]
Bouteraa, Y.; Ben Abdallah, I.; Ghommam, J. Task-space region-reaching control for medical robot manipulator. Comput. Electr. Eng., 2018, 67, 629-645.
[http://dx.doi.org/10.1016/j.compeleceng.2017.02.004]
[60]
Desai, J.P.; Sheng, J.; Cheng, S.S.; Wang, X.; Deaton, N.J.; Rahman, N. Towards patient-specific 3D-printed robotic systems for surgical interventions. IEEE Trans. Med. Robot. Bionics, 2019, 1(2), 77-87.
[http://dx.doi.org/10.1109/TMRB.2019.2912444] [PMID: 32984777]
[61]
Cheah, W.C.; Watson, S.A.; Lennox, B. Limitations of wireless power transfer technologies for mobile robots. Wirel. Power Transf., 2019, 6(2), 175-189.
[http://dx.doi.org/10.1017/wpt.2019.8]
[62]
Park, J. YeJi, H.; Duck Hee. Lee; Jaesoon, C. Study on safety and performance evaluation of micro - surgical robots based on open robot platform. J. Biomed. Eng. Res., 2019, 40(5), 206-214.
[http://dx.doi.org/10.9718/JBER.2019.40.5.206]
[63]
Chinzei, K. Safety of surgical robots and IEC 80601-2-77: The first international standard for surgical robots. Acta Polytech. Hungarica1, 6(8), 2019-2171.
[64]
Petrescu, R.V.V. Medical service of robots. J. Mechatronics Robot., 2019, 3(1), 60-81.
[http://dx.doi.org/10.3844/jmrsp.2019.60.81]
[65]
Xu, X.W.; Wu, X.X.; Jiang, X.G.; Xu, K.J.; Ying, L.J.; Ma, C.L.; Li, S.B.; Wang, H.Y.; Zhang, S.; Gao, H.N.; Sheng, J.F.; Cai, H.L.; Qiu, Y.Q.; Li, L.J. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-CoV-2) outside of Wuhan, China: Retrospective case series. BMJ, 2020, 368, m606.
[http://dx.doi.org/10.1136/bmj.m606] [PMID: 32075786]
[66]
Gunes, O.; Gunes, G.; Seyitoglu, D.C. The use of artificial intelligence in different medical branches: An overview of the literature. Med. Sci., 2019, 8(3), 770-773.
[http://dx.doi.org/10.5455/medscience.2019.08.9036]
[67]
Fu, L.; Wang, B.; Yuan, T.; Chen, X.; Ao, Y.; Fitzpatrick, T.; Li, P.; Zhou, Y.; Lin, Y.F.; Duan, Q.; Luo, G.; Fan, S.; Lu, Y.; Feng, A.; Zhan, Y.; Liang, B.; Cai, W.; Zhang, L.; Du, X.; Li, L.; Shu, Y.; Zou, H. Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: A systematic review and meta-analysis. J. Infect., 2020, 80(6), 656-665.
[http://dx.doi.org/10.1016/j.jinf.2020.03.041] [PMID: 32283155]
[68]
Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X.; Cheng, Z.; Yu, T.; Xia, J.; Wei, Y.; Wu, W.; Xie, X.; Yin, W.; Li, H.; Liu, M.; Xiao, Y.; Gao, H.; Guo, L.; Xie, J.; Wang, G.; Jiang, R.; Gao, Z.; Jin, Q.; Wang, J.; Cao, B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, 2020, 395(10223), 497-506.
[http://dx.doi.org/10.1016/S0140-6736(20)30183-5] [PMID: 31986264]
[69]
Samani, H.; Zhu, R. Robotic automated external defibrillator ambulance for emergency medical service in smart cities. IEEE Access, 2016, 4, 268-283.
[http://dx.doi.org/10.1109/ACCESS.2016.2514263]
[70]
Koceska, N.; Koceski, S.; Beomonte Zobel, P.; Trajkovik, V.; Garcia, N. A telemedicine robot system for assisted and independent liv-ing. Sensors, 2019, 19(4), 834.
[http://dx.doi.org/10.3390/s19040834] [PMID: 30781647]
[71]
Ozkil, A.G.; Fan, Z.; Dawids, S.; Aanes, H.; Kristensen, J.K.; Christensen, K.H. Service robots for hospitals: A case study of transporta-tion tasks in a hospital. Proc. 2009 IEEE Int. Conf. Autom. Logist. ICAL, 2009, pp. 289-294.
[http://dx.doi.org/10.1109/ICAL.2009.5262912]
[72]
Mettler, T.; Sprenger, M.; Winter, R. Service robots in hospitals. Eur. J. Inf. Syst., 2017, 26(5), 451-468.
[http://dx.doi.org/10.1057/s41303-017-0046-1]
[73]
El-Rashidy, N.; El-Sappagh, S.; Islam, S.M.R.; M El-Bakry, H.; Abdelrazek, S. Mobile health in remote patient monitoring for chronic diseases: Principles, trends, and challenges. Diagnostics, 2021, 11(4), 607.
[http://dx.doi.org/10.3390/diagnostics11040607] [PMID: 33805471]
[74]
Grespan, L.; Fiorini, P.; Colucci, G. Surgical Robots. In: The Route to Patient Safety in Robotic Surgery. Springer Tracts in Advanced Robotics, Springer, Cham; , 2019; 126, pp. 25-35.
[http://dx.doi.org/10.1007/978-3-030-03020-9_3]
[75]
Prassler, E.; Ritter, A.; Schaeffer, C.; Fiorini, P. A short history of cleaning robots. In: Autonomous Robots Springer; , 2000; 9, pp. 211-226.
[http://dx.doi.org/10.1023/A:1008974515925]
[76]
Wang, X.; Peng, Y.; Lu, L.; Lu, Z.; Bagheri, M.; Summers, R.M. ChestX-Ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, 2017 IEEE conference on computer vision and pattern recognition; CVPR, 2017, pp. 3462-3471.
[http://dx.doi.org/10.1109/CVPR.2017.369]
[77]
Twin robotic X-ray scanner – multitom rax Available from: https://www.siemens-healthineers.com/en-in/robotic-x-ray/twin-robotic-x-ray/multitom-rax (Accessed on August 14, 2021).
[78]
Siemens healthcare introduces first twin robotic X-ray Available from: biospectrumindia.com/news/75/6799/siemens-healthcare-introduces-first-twin-robotic-x-ray-system.html
[79]
Tsui, K.M.; Yanco, H.A. Assistive, rehabilitation, and surgical robots from the perspective of medical and healthcare professionals., Available from: https://www.aaai.org/Papers/Workshops/2007/WS-07-07/WS07-07-007.pdf (Accessed on August 14, 2021).
[80]
Rajasekharan, C.; Anu, J.; Neeraj, V.; Parvathy, R. Diagnosing scrub typhus: Meticulous physical examination is the key. BMJ Case Rep., 2014.
[http://dx.doi.org/10.1136/bcr-2014-204695]
[81]
Flirtey real-time delivery by flying robots. Available from: https://www.flirtey.com/ (Accessed on September 19, 2021).
[82]
Starship. Available from: https://www.starship.xyz/ (Accessed on September 09, 2021).
[83]
Nam, J.G.; Park, S.; Hwang, E.J.; Lee, J.H.; Jin, K.N.; Lim, K.Y.; Vu, T.H.; Sohn, J.H.; Hwang, S.; Goo, J.M.; Park, C.M. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology, 2019, 290(1), 218-228.
[http://dx.doi.org/10.1148/radiol.2018180237] [PMID: 30251934]
[84]
Haleem, A.; Javaid, M.; Vaishya, R. Effects of COVID-19 pandemic in daily life. Curr. Med. Res. Pract., 2020, 10(2), 78-79.
[http://dx.doi.org/10.1016/j.cmrp.2020.03.011] [PMID: 32292804]
[85]
Diaz-Escobar, J.; Ordóñez-Guillén, N.E.; Villarreal-Reyes, S.; Galaviz-Mosqueda, A.; Kober, V.; Rivera-Rodriguez, R.; Lozano Rizk, J.E. Deep-learning based detection of COVID-19 using lung ultrasound imagery. PLoS One, 2021, 16(8), e0255886.
[http://dx.doi.org/10.1371/journal.pone.0255886] [PMID: 34388187]
[86]
Gozes, O.; Frid-Adar, M.; Greenspan, H.; Browning, P.D.; Zhang, H.; Ji, W.; Bernheim, A.; Siegel, E. Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis; ArXiv, 2020.
[87]
Stebbing, J.; Phelan, A.; Griffin, I.; Tucker, C.; Oechsle, O.; Smith, D.; Richardson, P. COVID-19: Combining antiviral and anti-inflammatory treatments. Lancet Infect. Dis., 2020, 20(4), 400-402.
[http://dx.doi.org/10.1016/S1473-3099(20)30132-8] [PMID: 32113509]
[88]
Vaishya, R.; Javaid, M.; Khan, I.H.; Haleem, A. Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab. Syndr., 2020, 14(4), 337-339.
[http://dx.doi.org/10.1016/j.dsx.2020.04.012] [PMID: 32305024]
[89]
Chen, S.; Yang, J.; Yang, W.; Wang, C.; Bärnighausen, T. COVID-19 control in China during mass population movements at new year. Lancet, 2020, 395, 764-766.
[http://dx.doi.org/10.1016/S0140-6736(20)30421-9]
[90]
Pirouz, B.; Haghshenas, S.S.; Haghshenas, S.S.; Piro, P. Investigating a serious challenge in the sustainable development process: Analy-sis of confirmed cases of COVID-19 (new type of coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustain., 2020, 12(6), 2427.
[http://dx.doi.org/10.3390/su12062427]

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