CoChat
Sign Up
Cited by 199Artificial Intelligence in Healthcare and EducationHealthcare during COVID-19 PandemicRobotics and Automated Systems

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: a systematic review

Open access
Sarker, Sujan, Jamal, Lafifa, Ahmed, Syeda Faiza, Irtisam, Niloy·2021·Robotics and Autonomous Systems
DOI 10.1016/j.robot.2021.103902PMC PMC8493645
Open this paper in CoChat — free account

Abstract

The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.robot.2021.103902

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Deep learning analysis of medical images can help doctors identify subtle changes in the images that may indicate the presence of disease.

    “This can help doctors to identify subtle changes in the images that may indicate the presence of disease [12] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [12]

    The COVID-19 diagnosis section says AI helps radiologists analyse image patterns and assess the probability of infection, and describes deep-learning models that detect COVID-19 in chest images. It does not say the changes are subtle, and its discussion is focused on COVID-19 rather than disease generally (page not provided). (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 115

  1. [1]Cucinotta D. Vanelli M. WHO declares COVID-19 a pandemic Acta Bio Med.: Atenei Parm. 91 1 2020 157 PMC7569573 32191675
  2. [2]Liu Y. Gayle A.A. Wilder-Smith A. Rocklöv J. The reproductive number of COVID-19 is higher compared to SARS coronavirus J. Travel Med. 2020 1 4 PMC7074654 32052846
  3. [3]Meyerowitz-Katz G. Merone L. A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates Int. J. Infect. Dis. 101 2020 138 148 33007452 PMC7524446
  4. [4]Mehra M. Sharma R. Devi D. A brief review on epidemic to pandemic: Novel corona virus (COVID-19) J. Xian Univ. Archit. Technol. XII IV 2020 3795 3803
  5. [7]Sümen A. Adibelli D. The effect of coronavirus (COVID-19) outbreak on the mental well-being and mental health of individuals Perspect. Psychiatr. Care 57 3 2020 1041 1051 33103787
  6. [10]Albahri O. Zaidan A. Albahri A. Zaidan B. Abdulkareem K.H. Al-qaysi Z. Alamoodi A. Aleesa A. Chyad M. Alesa R. Kem L. Lakulu M.M. Ibrahim A. Rashid N.A. Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects J. Infect. Public Health 13 10 2020 1381 1396 32646771 PMC7328559
  7. [11]Hashmi H.A.S. Asif H.M. Early detection and assessment of covid-19 Front. Med. 7 2020 311 PMC7296153 32582748
  8. [13]Huang H. Chai P. Ehmke C. Merewether G. Dadabhoy F. Feng A. Li C. Thomas A. da Silva M. Raibert M. Agile mobile robotic platform for contactless vital signs monitoring TechRxiv 10 2020 PMC9096356 35571871
  9. [14]Li G. Xiong Z. Zhou H. Xie J. Chen W. Zhou M. Zhu Z. Zhou G. Liu J. Value of CT application in the screening, diagnosis, and treatment of COVID-19 J. Cent. South Univ. (Med. Sci.) 45 3 2020 32386018
  10. [15]Ozsahin I. Sekeroglu B. Musa M.S. Mustapha M.T. Uzun Ozsahin D. Review on diagnosis of COVID-19 from chest CT images using artificial intelligence Comput. Math. Methods Med. 2020 2020 PMC7519983 33014121
  11. [16]Song J. Wang H. Liu Y. Wu W. Dai G. Wu Z. Zhu P. Zhang W. Yeom K.W. Deng K. End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT Eur. J. Nucl. Med. Mol. Imaging 47 11 2020 2516 2524 32567006 PMC7306401
  12. [17]Xie J. Wang M. Liu R. Deep learning-based COVID-19 diagnosis and trend predictions Intelligent Systems and Methods to Combat Covid-19 2020 Springer 57 64
  13. [18]Zhou L. Li Z. Zhou J. Li H. Chen Y. Huang Y. Xie D. Zhao L. Fan M. Hashmi S. A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based COVID-19 diagnosis IEEE Trans. Med. Imaging 39 8 2020 2638 2652 32730214 PMC8769013
  14. [19]Loey M. Manogaran G. Khalifa N.E.M. A deep transfer learning model with classical data augmentation and cgan to detect covid-19 from chest ct radiography digital images Neural Comput. Appl. 2020 1 13 PMC7586204 33132536
  15. [20]Ardakani A.A. Kanafi A.R. Acharya U.R. Khadem N. Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks Comput. Biol. Med. 121 2020 103795 PMC7190523 32568676
  16. [21]Khadidos A. Khadidos A.O. Kannan S. Natarajan Y. Mohanty S.N. Tsaramirsis G. Analysis of COVID-19 infections on a CT image using DeepSense model Front. Public Health 8 2020 751 PMC7714903 33330341
  17. [22]Pham T.D. A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks Sci. Rep. 10 1 2020 1 8 33037291 PMC7547710
  18. [23]Huang Z. Liu X. Wang R. Zhang M. Zeng X. Liu J. Yang Y. Liu X. Zheng H. Liang D. FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms Appl. Intell. 2020 1 12 PMC7665967 34764567
  19. [24]Li L. Qin L. Xu Z. Yin Y. Wang X. Kong B. Bai J. Lu Y. Fang Z. Song Q. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT Radiology 296 2 2020 E65 -E71 32191588 PMC7233473
  20. [25]Wang S.-H. Govindaraj V.V. Górriz J.M. Zhang X. Zhang Y.-D. Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network Inf. Fusion 67 2021 208 229 33052196 PMC7544601
  21. [26]Mantas J. Setting up an easy-to-use machine learning pipeline for medical decision support: A case study for COVID-19 diagnosis based on deep learning with CT scans The Importance of Health Informatics in Public Health During a Pandemic, Vol. 272 2020 IOS Press 13 32604588
  22. [28]Mei X. Lee H.-C. Diao K.-y. Huang M. Lin B. Liu C. Xie Z. Ma Y. Robson P. Chung M. Bernheim A. Mani V. Calcagno C. Li K. Li S. Shan H. Lv J. Zhao T. Xia J. Yang Y. Artificial intelligence–enabled rapid diagnosis of patients with COVID-19 Nat. Med. 2020 1 5 32427924 PMC7446729
  23. [29]Lagos-Ortiz K. Jácome-Murillo E. Aguirre-Munizaga M. Medina-Moreira J. Analysis of the services generated through mobile applications for an accurate diagnosis of epidemiological metrics related to Covid-19 International Conference on Technologies and Innovation 2020 Springer 151 165
  24. [30]Rao A.S.S. Vazquez J.A. Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine Infect. Control Hosp. Epidemiol. 41 7 2020 826 830 32122430 PMC7200852
  25. [31]Yang Y. Lure F.Y. Miao H. Zhang Z. Jaeger S. Liu J. Guo L. Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections J. X-Ray Sci. Technol. 29 1 2020 1 17 PMC7990455 33164982
  26. [32]Sotoudeh H. Tabatabaei M. Tasorian B. Tavakol K. Sotoudeh E. Moini A.L. Artificial intelligence empowers radiologists to differentiate pneumonia induced by COVID-19 versus influenza viruses Acta Inform. Med. 28 3 2020 190 33417642 PMC7780838
  27. [33]Qjidaa M. Ben-Fares A. Mechbal Y. Amakdouf H. Maaroufi M. Alami B. Qjidaa H. Development of a clinical decision support system for the early detection of COVID-19 using deep learning based on chest radiographic images 2020 International Conference on Intelligent Systems and Computer Vision (ISCV) 2020 IEEE 1 6
  28. [34]Chiu W.H.K. Vardhanabhuti V. Poplavskiy D. Yu P.L.H. Du R. Yap A.Y.H. Zhang S. Fong A.H.-T. Chin T.W.-Y. Lee J.C.Y. Detection of COVID-19 using deep learning algorithms on chest radiographs J. Thorac. Imaging 35 6 2020 369 376 32969949
  29. [36]Kongmanee J. Thanapattheerakul T. Fine-tuning a lightweight convolutional neural networks for COVID-19 diagnosis CSBio’20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics 2020 101 103
  30. [37]Haritha D. Swaroop N. Mounika M. Prediction of COVID-19 cases using CNN with X-rays 2020 5th International Conference on Computing, Communication and Security (ICCCS) 2020 IEEE 1 6
  31. [40]Elgendi M. Nasir M.U. Tang Q. Fletcher R.R. Howard N. Menon C. Ward R. Parker W. Nicolaou S. The performance of deep neural networks in differentiating chest X-rays of COVID-19 patients from other bacterial and viral pneumonias Front. Med. 7 2020 550 PMC7461795 33015100
  32. [41]Al-antari M.A. Hua C.-H. Bang J. Lee S. Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images Appl. Intell. 2020 1 18 PMC7695589 34764573
  33. [42]Padma T. Kumari C.U. Deep learning based chest X-Ray image as a diagnostic tool for COVID-19 2020 International Conference on Smart Electronics and Communication (ICOSEC) 2020 IEEE 589 592
  34. [43]Brunese L. Martinelli F. Mercaldo F. Santone A. Machine learning for coronavirus covid-19 detection from chest x-rays Procedia Comput. Sci. 176 2020 2212 2221 33042308 PMC7531990
  35. [44]Shankar K. Perumal E. A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images Complex Intell. Syst. 2020 1 17 PMC7659408 34777955
  36. [45]Pham T.D. Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning? Health Inf. Sci. Syst. 9 1 2021 1 11 33235710 PMC7680558
  37. [46]Yasar H. Ceylan M. A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks Appl. Intell. 2020 1 24 PMC7609830 34764560
  38. [47]Gupta A. Gupta S. Katarya R. InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray Appl. Soft Comput. 2020 106859 PMC7598372 33162872
  39. [50]Sharma A. Rani S. Gupta D. Artificial intelligence-based classification of chest X-ray images into COVID-19 and other infectious diseases Int. J. Biomed. Imaging 2020 2020 91916 91923 PMC7539085 33061946
  40. [52]Rajaraman S. Antani S. Weakly labeled data augmentation for deep learning: A study on covid-19 detection in chest x-rays Diagnostics 10 6 2020 358 32486140 PMC7345787
  41. [53]Ouchicha C. Ammor O. Meknassi M. CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images Chaos Solitons Fractals 140 2020 110245 PMC7472981 32921934
  42. [56]Zhang R. Tie X. Qi Z. Bevins N.B. Zhang C. Griner D. Song T.K. Nadig J.D. Schiebler M.L. Garrett J.W. Diagnosis of coronavirus disease 2019 pneumonia by using chest radiography: Value of artificial intelligence Radiology 298 2 2021 E88 E97 32969761 PMC7841876
  43. [57]Toğaçar M. Ergen B. Cömert Z. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches Comput. Biol. Med. 121 2020 103805 PMC7202857 32568679
  44. [58]Hassantabar S. Ahmadi M. Sharifi A. Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches Chaos Solitons Fractals 140 2020 110170 PMC7388764 32834651
  45. [59]Omoniyi T. Alabere H. Sule E. Diagnosis of COVID-19 using artificial intelligence based model Journal of Physics: Conference Series, Vol. 1734 2021 IOP Publishing 012007
  46. [63]Vinod D.N. Prabaharan S. Data science and the role of artificial intelligence in achieving the fast diagnosis of Covid-19 Chaos Solitons Fractals 140 2020 110182 PMC7392156 32834658
  47. [64]Nath M.K. Kanhe A. Mishra M. A novel deep learning approach for classification of COVID-19 images 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) 2020 IEEE 752 757
  48. [65]Hassan A. Shahin I. Alsabek M.B. Covid-19 detection system using recurrent neural networks 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) 2020 IEEE 1 5
  49. [66]Laguarta J. Hueto F. Subirana B. COVID-19 artificial intelligence diagnosis using only cough recordings IEEE Open J. Eng. Med. Biol. 1 2020 275 281 34812418 PMC8545024
  50. [67]Imran A. Posokhova I. Qureshi H.N. Masood U. Riaz M.S. Ali K. John C.N. Hussain M.I. Nabeel M. AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app Inform. Med. Unlocked 20 2020 100378 PMC7318970 32839734
  51. [68]Güemes A. Ray S. Aboumerhi K. Desjardins M.R. Kvit A. Corrigan A.E. Fries B. Shields T. Stevens R.D. Curriero F.C. A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States Sci. Rep. 11 1 2021 1 11 33633250 PMC7907397
  52. [69]Nguyen T. Zoëga Andreasen S. Wolff A. Duong Bang D. From lab on a chip to point of care devices: The role of open source microcontrollers Micromachines 9 8 2018 403 30424336 PMC6187319
  53. [70]Nguyen T. Duong Bang D. Wolff A. 2019 novel coronavirus disease (COVID-19): paving the road for rapid detection and point-of-care diagnostics Micromachines 11 3 2020 306 32183357 PMC7142866
  54. [71]Taha B.A. Al Mashhadany Y. Hafiz Mokhtar M.H. Dzulkefly Bin Zan M.S. Arsad N. An analysis review of detection coronavirus disease 2019 (COVID-19) based on biosensor application Sensors 20 23 2020 6764 33256085 PMC7729752
  55. [72]Wong C.K. Ho D.T.Y. Tam A.R. Zhou M. Lau Y.M. Tang M.O.Y. Tong R.C.F. Rajput K.S. Chen G. Chan S.C. Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial BMJ Open 10 7 2020 e038555 PMC7380847 32699167
  56. [73]Orozco-del Castillo M. Novelo-Cruz R. Hernández-Gómez J. Mena-Zapata P. Brito-Borges E. Álvarez-Pacheco A. García-Gutiérrez A. Yáñez-Casas G. Fuzzy logic-based COVID-19 and other respiratory conditions pre-clinical diagnosis system International Congress of Telematics and Computing 2020 Springer 402 419
  57. [76]Leventhal R. Care management for the COVID-19 vulnerable: Medical home network surges forward 2021 Last Accessed: 17 January 2021. URL https://medicalhomenetwork.org/press/care-management-for-the-covid-19-vulnerable-medical-home-network-surges-forward
  58. [77]Lessmann N. Sánchez C.I. Beenen L. Boulogne L.H. Brink M. Calli E. Charbonnier J.-P. Dofferhoff T. van Everdingen W.M. Gerke P.K. Automated assessment of COVID-19 reporting and data system and chest CT severity scores in patients suspected of having COVID-19 using artificial intelligence Radiology 298 1 2021 E18 E28 32729810 PMC7393955
  59. [78]Xiao L.-s. Li P. Sun F. Zhang Y. Xu C. Zhu H. Cai F.-Q. He Y.-L. Zhang W.-F. Ma S.-C. Development and validation of a deep learning-based model using computed tomography imaging for predicting disease severity of coronavirus disease 2019 Front. Bioeng. Biotechnol. 8 2020 898 32850746 PMC7411489
  60. [79]Zhu J. Shen B. Abbasi A. Hoshmand-Kochi M. Li H. Duong T.Q. Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs PLoS One 15 7 2020 e0236621 PMC7386587 32722697
  61. [80]Abdulaal A. Patel A. Charani E. Denny S. Mughal N. Moore L. Prognostic modeling of COVID-19 using artificial intelligence in the United Kingdom: model development and validation J. Med. Internet Res. 22 8 2020 e20259 PMC7451108 32735549
  62. [81]Meng L. Dong D. Li L. Niu M. Bai Y. Wang M. Qiu X. Zha Y. Tian J. A deep learning prognosis model help alert for COVID-19 patients at high-risk of death: A multi-center study IEEE J. Biomed. Health Inf. 24 12 2020 3576 3584 PMC8545180 33108303
  63. [82]Ko H. Chung H. Kang W.S. Park C. Kim S.E. Chung C.R. Ko R.E. Lee H. Seo J.H. Choi T.-Y. An artificial intelligence model to predict the mortality of COVID-19 patients at hospital admission time using routine blood samples: Development and validation of an ensemble model J. Med. Internet Res. 22 12 2020 e25442 PMC7759509 33301414
  64. [83]Mushtaq J. Pennella R. Lavalle S. Colarieti A. Steidler S. Martinenghi C.M. Palumbo D. Esposito A. Rovere-Querini P. Tresoldi M. Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients Eur. Radiol. 2020 1 10 PMC7499014 32945968
  65. [84]Muhammad L. Islam M.M. Usman S.S. Ayon S.I. Predictive data mining models for novel coronavirus (COVID-19) infected patients’ recovery SN Comput. Sci. 1 4 2020 1 7 PMC7306186 33063049
  66. [85]Lai L. Wittbold K.A. Dadabhoy F.Z. Sato R. Landman A.B. Schwamm L.H. He S. Patel R. Wei N. Zuccotti G. Digital triage: Novel strategies for population health management in response to the COVID-19 pandemic Healthcare, Vol. 8 2020 Elsevier 100493 PMC7586929 33129176
  67. [87]Iwendi C. Bashir A.K. Peshkar A. Sujatha R. Chatterjee J.M. Pasupuleti S. Mishra R. Pillai S. Jo O. COVID-19 patient health prediction using boosted random forest algorithm Front. Public Health 8 2020 357 32719767 PMC7350612
  68. [88]Assaf D. Gutman Y. Neuman Y. Segal G. Amit S. Gefen-Halevi S. Shilo N. Epstein A. Mor-Cohen R. Biber A. Utilization of machine-learning models to accurately predict the risk for critical COVID-19 Intern. Emerg. Med. 15 8 2020 1435 1443 32812204 PMC7433773
  69. [89]Zhu J.S. Ge P. Jiang C. Zhang Y. Li X. Zhao Z. Zhang L. Duong T.Q. Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients J. Am. Coll. Emerg. Physicians Open 1 6 2020 1364 1373 32838390 PMC7405082
  70. [90]Chao H. Fang X. Zhang J. Homayounieh F. Arru C.D. Digumarthy S.R. Babaei R. Mobin H.K. Mohseni I. Saba L. Integrative analysis for COVID-19 patient outcome prediction Med. Image Anal. 67 2021 101844 PMC7553063 33091743
  71. [92]McRae M.P. Dapkins I.P. Sharif I. Anderman J. Fenyo D. Sinokrot O. Kang S.K. Christodoulides N.J. Vurmaz D. Simmons G.W. Managing COVID-19 with a clinical decision support tool in a community health network: Algorithm development and validation J. Med. Internet Res. 22 8 2020 e22033 PMC7446714 32750010
  72. [93]Kwon Y.J. Toussie D. Finkelstein M. Cedillo M.A. Maron S.Z. Manna S. Voutsinas N. Eber C. Jacobi A. Bernheim A. Combining initial radiographs and clinical variables improves deep learning prognostication of patients with COVID-19 from the emergency department Radiol.: Artif. Intell. 2020 e200098 PMC7754832 33928257
  73. [94]Trebeschi S. Drago S. Birkbak N. Kurilova I. Cǎlin A. Pizzi A.D. Lalezari F. Lambregts D. Rohaan M. Parmar C. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers Ann. Oncol. 30 6 2019 998 1004 30895304 PMC6594459
  74. [95]Shachar C. Gerke S. Adashi E.Y. AI surveillance during pandemics: Ethical implementation imperatives Hastings Cent. Rep. 50 3 2020 18 21 PMC7361418 32596887
  75. [96]Hertelendy A.J. Goniewicz K. Khorram-Manesh A. The COVID-19 pandemic: how predictive analysis, artificial intelligence and GIS can be integrated into a clinical command system to improve disaster response and preparedness Am. J. Emerg. Med. 45 2020 671 672 33160803 PMC7605801
  76. [98]Brown N.J. A novel peer-to-peer contact tracking application for COVID-19 and future pandemics Diabetes Metab. Syndr.: Clin. Res. Rev. 14 5 2020 773 774 PMC7271867 32526624
  77. [99]Hirose H. Wang L. Prediction of infectious disease spread using Twitter: A case of influenza 2012 Fifth International Symposium on Parallel Architectures, Algorithms and Programming 2012 100 105
  78. [100]Joshi A. Dey N. Santosh K. Intelligent Systems and Methods to Combat Covid-19 2020 Springer
  79. [101]Medrek M. Pastuszak Z. Numerical simulation of the novel coronavirus spreading Expert Syst. Appl. 166 2021 114109 PMC7557303 33078047
  80. [104]Nguyen C.T. Saputra Y.M. Van Huynh N. Nguyen N.-T. Khoa T.V. Tuan B.M. Nguyen D.N. Hoang D.T. Vu T.X. Dutkiewicz E. A comprehensive survey of enabling and emerging technologies for social distancing—Part I: Fundamentals and enabling technologies IEEE Access 8 2020 153479 153507 34812349 PMC8545308
  81. [105]Hameed B.Z. Patil V. Shetty D.K. Naik N. Nagaraj N. Sharma D. Use of artificial intelligence-based computer vision system to practice social distancing in hospitals to prevent transmission of COVID-19 Indian J. Commun. Med.: Off. Publ. Indian Assoc. Prevent. Soc. Med. 45 3 2020 379 PMC7745826 33354024
  82. [106]Ahmed I. Ahmad M. Rodrigues J.J. Jeon G. Din S. A deep learning-based social distance monitoring framework for COVID-19 Sustainable Cities Soc. 65 2021 102571 PMC7603992 33163330
  83. [110]Battineni G. Chintalapudi N. Amenta F. AI Chatbot design during an epidemic like the novel coronavirus Healthcare, Vol. 8 2020 Multidisciplinary Digital Publishing Institute 154 PMC7349073 32503298
  84. [111]Erazo W.S. Guerrero G.P. Betancourt C.C. Salazar I.S. Chatbot implementation to collect data on possible COVID-19 cases and release the pressure on the primary health care system 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) 2020 IEEE 0302 0307
  85. [112]Pranathi B. Nair A. Anushree C. Chandar T. Sahayantra-A patient assistance robot 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) 2020 IEEE 1 6
  86. [113]Lafranca L. Li J. Humans and robots in times of quarantine based on first-hand accounts International Conference on Social Robotics 2020 Springer 688 707
  87. [114]Su C.-Y. Samani H. Yang C.-Y. Fernando O.N.N. Doctor robot with physical examination for skin disease diagnosis and telemedicine application 2018 International Conference on System Science and Engineering (ICSSE) 2018 IEEE 1 6
  88. [115]Tsai S.-C. Samani H. Kao Y.-W. Zhu K. Jalaian B. Design and development of interactive intelligent medical agent 2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) 2018 IEEE 210 215
  89. [116]Shinkariov S. Zingerman B. Kargalskaya I. Nozik A. Fistul I. Evelson L. Kremenetskaya A. Sun L. Xu J. Kremenetskaya O. Telemedicine system with elements of artificial intelligence for health monitoring during COVID-19 pandemic International Conference on Health Information Science 2020 Springer 103 110
  90. [118]Wang J. Peng C. Zhao Y. Ye R. Hong J. Huang H. Chen L. Application of a robotic tele-echography system for COVID-19 pneumonia J. Ultrasound Med. 40 2 2021 385 390 32725833
  91. [121]Jat D.S. Singh C. Artificial intelligence-enabled robotic drones for COVID-19 outbreak Intelligent Systems and Methods to Combat Covid-19 2020 Springer 37 46
  92. [122]Kumar A. Sharma K. Singh H. Naugriya S.G. Gill S.S. Buyya R. A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic Future Gener. Comput. Syst. 115 2021 1 19 32895585 PMC7467876
  93. [123]Sarbadhikari S.N. Pradhan K.B. The need for developing technology-enabled, safe, and ethical workforce for healthcare delivery Saf. Health Work 11 4 2020 533 536 32839672 PMC7438991
  94. [124]Li B.Y. Lin H. Samani H. Sadler L. Gregory T. Jalaian B. On 3D autonomous delivery systems: Design and development 2017 International Conference on Advanced Robotics and Intelligent Systems (ARIS) 2017 IEEE 1 6
  95. [128]Shen Y. Guo D. Long F. Mateos L.A. Ding H. Xiu Z. Hellman R.B. King A. Chen S. Zhang C. Robots under COVID-19 pandemic: A comprehensive survey IEEE Access 9 2020 1590 1615 34976569 PMC8675561
  96. [132]Nassir A.A. Baptiste M.J. Mwikarago I. Habimana M.R. Ndinkabandi J. Murangwa A. Nyatanyi T. Muvunyi C.M. Nsanzimana S. Leon M. Rpa-based method for the detection of SARS-CoV2 2020 MedRxiv
  97. [140]Begić A. Application of service robots for disinfection in medical institutions International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies 2017 Springer 1056 1065
  98. [141]Sayed A.S. Ammar H.H. Shalaby R. Centralized multi-agent mobile robots SLAM and navigation for COVID-19 field hospitals 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) 2020 IEEE 444 449
  99. [142]Muthugala M. Samarakoon S. Mohan Rayguru M. Ramalingam B. Elara M.R. Wall-following behavior for a disinfection robot using type 1 and type 2 fuzzy logic systems Sensors 20 16 2020 4445 32784888 PMC7472486
  100. [149]Ekins S. Freundlich J.S. Clark A.M. Anantpadma M. Davey R.A. Madrid P. Machine learning models identify molecules active against the Ebola virus in vitro F1000Research 4 2015 1091 26834994 PMC4706063
  101. [150]Zhang L. Ai H.-X. Li S.-M. Qi M.-Y. Zhao J. Zhao Q. Liu H.-S. Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function Oncotarget 8 47 2017 83142 83154 29137330 PMC5669956
  102. [151]Ho D. Addressing COVID-19 drug development with artificial intelligence Adv. Intell. Syst. 2 5 2020 2000070 PMC7235485 32838299
  103. [153]Scudellari M. Researchers are betting that AI and automation can cut drug discovery from five years to six months: Automating antivirals IEEE Spectr. 57 10 2020 44 49
  104. [154]Caruso F.P. Scala G. Cerulo L. Ceccarelli M. A review of COVID-19 biomarkers and drug targets: resources and tools Brief. Bioinform. 22 2 2021 701 713 33279954 PMC7799271
  105. [157]Fauqueur J. Thillaisundaram A. Togia T. Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns 2019 arXiv preprint arXiv:1907.01417 · arXiv
  106. [158]Richardson P. Griffin I. Tucker C. Smith D. Oechsle O. Phelan A. Rawling M. Savory E. Stebbing J. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease Lancet 395 10223 2020 e30 e31 32032529 PMC7137985
  107. [159]Zhang H. Saravanan K.M. Yang Y. Hossain M.T. Li J. Ren X. Pan Y. Wei Y. Deep learning based drug screening for novel coronavirus 2019-nCov Interdiscip. Sci.: Comput. Life Sci. 12 2020 368 376 PMC7266118 32488835
  108. [161]Ge Y. Tian T. Huang S. Wan F. Li J. Li S. Wang X. Yang H. Hong L. Wu N. An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19 Signal Trans. Target. Ther. 6 1 2021 1 16 PMC8065335 33895786
  109. [163]Dotolo S. Marabotti A. Facchiano A. Tagliaferri R. A review on drug repurposing applicable to COVID-19 Brief. Bioinform. 22 2 2021 726 741 33147623 PMC7665348
  110. [164]Mohanty S. Rashid M.H.A. Mridul M. Mohanty C. Swayamsiddha S. Application of artificial intelligence in COVID-19 drug repurposing Diabetes Metab. Syndr.: Clin. Res. Rev. 14 5 2020 1027 1031 PMC7332938 32634717
  111. [165]Bharadwaj K.K. Srivastava A. Panda M.K. Singh Y.D. Maharana R. Mandal K. Singh B.M. Singh D. Das M. Murmu D. Computational intelligence in vaccine design against COVID-19 Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis 2021 Springer 311 329
  112. [167]Russo G. Reche P. Pennisi M. Pappalardo F. The combination of artificial intelligence and systems biology for intelligent vaccine design Expert Opin. Drug Discovery 15 11 2020 1267 1281 32662677
  113. [168]Vinyals O. Blundell C. Lillicrap T. Wierstra D. Matching networks for one shot learning Adv. Neural Inf. Process. Syst. 29 2016 3630 3638
  114. [169]Xian Y. Lampert C.H. Schiele B. Akata Z. Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly IEEE Trans. Pattern Anal. Mach. Intell. 41 9 2018 2251 2265 30028691
  115. [171]Brown T.B. Mann B. Ryder N. Subbiah M. Kaplan J. Dhariwal P. Neelakantan A. Shyam P. Sastry G. Askell A. Language models are few-shot learners Adv. Neural Inf. Process. Syst. 33 2020 1877 1901

Cited by

Sorted by citations

Authors
Sarker, S., Jamal, L., Ahmed, S. F., & Irtisam, N.
Affiliations
University of Dhaka (BD)
Corresponding author
Jamal, Lafifa
ORCID
Venue
Robotics and Autonomous Systems
Publisher
Elsevier BV
Published
2021-12
Volume
146
Pages
103902
ISSN
0921-8890 (ISSN-L)
DOI
10.1016/j.robot.2021.103902
PMC
PMC8493645
OpenAlex
W3204134923
Cited by
199
References
5
Citations by year
2026: 12 · 2025: 46 · 2024: 65 · 2023: 40 · 2022: 35 · 2021: 1
FWCI
2.85
Type
journal article
Language
en
Open access
Licence
Locations
  • Robotics and Autonomous Systems (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Healthcare during COVID-19 Pandemic
  • Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
Keywords
COVID-19 pandemic, healthcare robotics, artificial intelligence in healthcare, telemedicine, disinfection robots, vaccine distribution, robotic process automation, mental health, risk assessment

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review