Artificial Intelligence and Robotics in Delivering Healthcare at the Time of COVID-19 Pandemic
Abstract
It has been more than six decades since Artificial Intelligence (AI) was introduced to outperform humans in accuracy and speed. Ever since, many algorithms have been developed that gained success in fulfilling the claims of AI. Their high speed and accuracy have made them perfect candidates for substituting humans in many settings. Even though AI has changed the face of many industries, its application in some others is still a subject of debate. Besides AI, which, over decades, has changed the face of industries for good, there is another phenomenon that has changed it: the novel coronavirus (COVID-19) pandemic. Unlike AI, this pandemic has changed every aspect of human life. Due to its fast spread through human contact, stay-inshelter orders have been placed to slow the person-to-person transmission. This is a source of concern for industries as many of them may fade away due to the pandemic. However, there is one industry at risk of burning out rather than fading away: the healthcare industry. Limited resources, on the one hand, and increased demand, on the other hand, have made the healthcare industry one of the main victims of the pandemic. Emergency departments are flooded with patients, yet non-emergent medical services have been nearly shut down. Therefore, solutions are sought to help both lighten the burden on emergency departments and facilitate providing non-emergent medical services. AI and automated systems can be the key to such solutions. They have proved their efficacy in many instances in the healthcare industry, from emergency department triage to assisting surgeries. Thus far, their widespread use has been halted due to legal and ethical debates. However, the COVID-19 pandemic can be a turning point in the integration of AI into the healthcare system, just as improving AI integration with healthcare can be a turning point in this pandemic. Herein, we overview how AI can help deliver non-emergent medical services during the pandemic and possibly thereafter
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12. Chapman WW, Christensen LM, Wagner MM, Haug PJ, Ivanov O, Dowling JN, et al. Classifying free-text triage chief complaints into syndromic categories with natural language processing. Artif Intell Med. 2005.
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14. Hemmerling TM, Taddei R, Wehbe M, Zaouter C, Cyr S, Morse J. First robotic tracheal intubations in humans using the Kepler intubation system. Br J Anaesth. 2012.
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18. Parwani AV. Next-generation diagnostic pathology: use of digital pathology and artificial intelligence tools to augment a pathological diagnosis. Diagn Pathol. 2019;14.
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22. Boman K, Olofsson M, Berggren P, Sengupta PP, Narula J. Robot-assisted remote echocardiographic examination and teleconsultation. JACC Cardiovasc Imaging. 2014.
23. Swerdlow DR, Cleary K, Wilson E, Azizi-Koutenaei B, Monfaredi R. Robotic arm-assisted sonography: review of technical developments and potential clinical applications. AJR Am J Roentgenol. 2017;208.
24. Guo J, Li H, Chen Y, Chen P, Li X, Sun S. Robotic ultrasound and ultrasonic robot. Endosc Ultrasound. 2019;8.
25. Chen A, Nikitczuk K, Nikitczuk J, Maguire T, Yarmush M. Portable robot for autonomous venipuncture using 3D near infrared image guidance. Technol Health Care. 2013.
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27. Yeung C, Cheung JL, Sreedhar B. Emerging next-generation robotic colonoscopy systems towards painless colonoscopy. J Dig Dis. 2019.
28. Chakravartti J, Rao SV. Robotic assisted percutaneous coronary intervention: hype or hope? J Am Heart Assoc. 2019.
29. Robotic-assisted percutaneous coronary intervention: rationale, implementation, case selection and limitations of current technology. J Clin Med. 2018.
30. Maor E, Eleid MF, Gulati R, Lerman A, Sandhu GS. Current and future use of robotic devices to perform percutaneous coronary interventions: a review. J Am Heart Assoc. 2017.
31. Siddamreddy S, Thotakura R, Dandu V, Kanuru S, Meegada S, et al. Corona Virus Disease 2019 (COVID-19) presenting as acute ST elevation myocardial infarction. Cureus. 2020.
32. Siripanthong B, Nazarian S, Muser D, Deo R, Santangeli P, Khanji MY, et al. Recognizing COVID-19–related myocarditis: The possible pathophysiology and proposed guideline for diagnosis and management. Heart Rhythm. 2020.
33. Al-Omar K, Bakkar S, Khasawneh L, Donatini G, Miccoli P, et al. Resuming elective surgery in the time of COVID-19: a safe and comprehensive strategy. Updates Surg. 2020.
34. Patel V, Jimenez E, Cornwell L, Tran T, Paniagua D, Denktas AE, et al. Cardiac surgery during the coronavirus disease 2019 pandemic: perioperative considerations and triage recommendations. 2020.
35. Rosenbaum L. The untold toll—The pandemic’s effects on patients without COVID-19. N Engl J Med. 2020.
36. Kutikov A, Weinberg DS, Edelman MJ, Horwitz EM, Uzzo RG, et al. A war on two fronts: cancer care in the time of COVID-19. Ann Intern Med. 2020;172.
37. Kwoh YS, Hou J, Jonckheere EA, Hayati S. A robot with improved absolute positioning accuracy for CT-guided stereotactic brain surgery. IEEE Trans Biomed Eng. 1988;35(2):153–60.
38. Santok GD, Rha KH. Robotic surgery: an evolution of future direction. Investig Clin Urol. 2016;57.
39. Zemmar A, Lozano AM, Nelson BJ. The rise of robots in surgical environments during COVID-19. Nat Mach Intell. 2020;2(10):566–72.
40. Candamourty R, Venkatachalam S, Ramesh Babu MR, Suresh Kumar G. Ludwig’s angina—an emergency: a case report with literature review. J Nat Sci Biol Med. 2012;3.
41. Wu Y, Wang F, Fan S, Chow JK. Robotics in dental implantology. Oral Maxillofac Surg Clin North Am. 2019.
42. Al Omran Y, Abdall-Razak A, Ghassemi N, Alomran S, Yang D, Ghanem AM. Robotics in cleft surgery: origins, current status, and future directions. Robot Surg Res Rev. 2019.
43. Dobbs TD, Cundy O, Samarendra H, Khan K, Whitaker IS. A systematic review of the role of robotics in plastic and reconstructive surgery—From inception to the future. Front Surg. 2017;4.
44. Khan ZH, Siddique A, Lee CW. Robotics utilization for healthcare digitization in global COVID-19 management. Int J Environ Res Public Health. 2020;17(11):3819.
45. Adamson AS, Smith A. Machine learning and healthcare disparities in dermatology. JAMA Dermatol. 2018;154:1247–8.
46. Langarizadeh M, Moghbeli F. Applying Naive Bayesian networks to disease prediction: systematic review. Acta Inform Med. 2016;24(5):364–9.
47. Stahl BC, Coeckelbergh M. Ethics of healthcare robotics: towards responsible research and innovation. Rob Auton Syst. 2016.
48. Su H, Hu Y, Li Z, Knoll A, Ferrigno G, De Momi E, et al. Reinforcement learning-based manipulation skill transferring for robot-assisted minimally invasive surgery. Proc IEEE Int Conf Robot Autom. 2020;2203–8.
49. Bach C, Miernik A, Schönthaler M. Training in robotics: the learning curve and contemporary concepts in training. Arab J Urol. 2014;12:58–61.
50. Mohamed K, Rodríguez-Román E, Rahmani F, Zhang H, Ivanovska M, Makka SA, et al. Borderless collaboration is needed for COVID-19—a disease that knows no borders. Infect Control Hosp Epidemiol. 2020;41.
51. Moradian N, Ochs HD, Sedikies C, Hamblin MR, Camargo CA, Martinez JA, et al. The urgent need for integrated science to fight COVID-19 pandemic and beyond. J Transl Med. 2020;18.
52. Chary M, Parikh S, Manini AF, Boyer EW, Radeos M. A review of natural language processing in medical education. West J Emerg Med. 2019.
53. Doan S, Bastarache L, Klimkowski S, Denny JC, Xu H. Integrating existing natural language processing tools for medication extraction from discharge summaries. J Am Med Inform Assoc. 2010;17(5):528–31.
54. Bajwa IS. Virtual telemedicine using natural language processing. Int J Inf Technol Web Eng. 2010.
55. Langarizadeh M, Moghbeli F. Applying Naive Bayesian networks to disease prediction: a systematic review. Acta Inform Med.
56. Lisboa PJG. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw. 2002;15.
57. Rossi L, Varriale A, Deleo G, Segramora VM. Machine learning and neural networks in vascular surgery. Eur J Vasc Endovasc Surg. 2019.
58. Tayeb S, Pirouz M, Sun J, Hall K, Chang A, Li J, et al. Toward predicting medical conditions using k-nearest neighbors. Proc IEEE Int Conf Big Data. 2017;3897–903.
59. Son YJ, Kim HG, Kim EH, Choi S, Lee SK, et al. Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc Inform Res. 2010.
2. He D, Zhao S, Li Y, Cao P, Gao D, Lou Y, et al. Comparing COVID-19 and the 1918–19 influenza pandemics in the United Kingdom. Int J Infect Dis. 2020;98.
3. Cockerham WC, Cockerham GB. Health and globalization. In: The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society. Chichester, UK: John Wiley & Sons, Ltd; 2014.
4. Tayarani-N MH. Applications of artificial intelligence in battling against COVID-19: a literature review. Chaos Solitons Fractals. 2020.
5. Jazieh AR, Kozlakidis Z. Healthcare transformation in the post-coronavirus pandemic era. Front Med. 2020.
6. Murashov V, Hearl F, Howard J. Working safely with robot workers: recommendations for the new workplace. J Occup Environ Hyg. 2016;13.
7. Schönberger D. Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications. Int J Law Inf Technol. 2019.
8. Zeng Z, Chen PJ, Lew AA. From high-touch to high-tech: COVID-19 drives robotics adoption. Tour Geogr. 2020;22(3):724–34. Available from: https://nau.pure.elsevier.com/en/publications/from-high-touch-to-high-tech-covid-19-drives-robotics-adoption.
9. Khan ZH, Siddique A, Lee CW. Robotics utilization for healthcare digitization in global COVID-19 management. Int J Environ Res Public Health. 2020.
10. Abad-Grau MM, Ierache J, Cervino C, Sebastiani P. Evolution and challenges in the design of computational systems for triage assistance. J Biomed Inform. 2008;41:432–41.
11. Michalowski W, Slowinski R, Wilk S, Farion KJ, Pike J, Rubin S. Design and development of a mobile system for supporting emergency triage. Methods Inf Med. 2005.
12. Chapman WW, Christensen LM, Wagner MM, Haug PJ, Ivanov O, Dowling JN, et al. Classifying free-text triage chief complaints into syndromic categories with natural language processing. Artif Intell Med. 2005.
13. Fotouhi Ghazvini MH, Vahabi M, Rasid MFA, Raja Abdullah RSA. Improvement of MAC performance for wireless sensor networks. Commun Comput Inf Sci. 2008.
14. Hemmerling TM, Taddei R, Wehbe M, Zaouter C, Cyr S, Morse J. First robotic tracheal intubations in humans using the Kepler intubation system. Br J Anaesth. 2012.
15. Zou L, Ge C, Wang ZJ, Cretu E, Li X. Novel tactile sensor technology and smart tactile sensing systems: a review. Sensors (Basel). 2017;17.
16. Li M, Luo S, Nanayakkara T, Seneviratne LD, Dasgupta P, Althoefer K. Multi-fingered haptic palpation using pneumatic feedback actuators. Sens Actuators A Phys. 2014;218:132–41.
17. Gomolin A, Netchiporouk E, Gniadecki R, Litvinov IV. Artificial intelligence applications in dermatology: where do we stand? Front Med. 2020;7.
18. Parwani AV. Next-generation diagnostic pathology: use of digital pathology and artificial intelligence tools to augment a pathological diagnosis. Diagn Pathol. 2019;14.
19. Askarian B, Yoo SC, Chong JW. Novel image processing method for detecting strep throat using a smartphone. Sensors (Basel). 2019.
20. Takahashi H, Tampo H, Arai Y, Inoue Y, Kawashima H. Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy. PLoS One. 2017.
21. Ranschaert ER, Morozov S, Algra PR. Artificial intelligence in medical imaging: opportunities, applications, and risks. Springer; 2019.
22. Boman K, Olofsson M, Berggren P, Sengupta PP, Narula J. Robot-assisted remote echocardiographic examination and teleconsultation. JACC Cardiovasc Imaging. 2014.
23. Swerdlow DR, Cleary K, Wilson E, Azizi-Koutenaei B, Monfaredi R. Robotic arm-assisted sonography: review of technical developments and potential clinical applications. AJR Am J Roentgenol. 2017;208.
24. Guo J, Li H, Chen Y, Chen P, Li X, Sun S. Robotic ultrasound and ultrasonic robot. Endosc Ultrasound. 2019;8.
25. Chen A, Nikitczuk K, Nikitczuk J, Maguire T, Yarmush M. Portable robot for autonomous venipuncture using 3D near infrared image guidance. Technol Health Care. 2013.
26. Yeung BPM, Chiu PWY. Application of robotics in gastrointestinal endoscopy: a review. World J Gastroenterol. 2016;22.
27. Yeung C, Cheung JL, Sreedhar B. Emerging next-generation robotic colonoscopy systems towards painless colonoscopy. J Dig Dis. 2019.
28. Chakravartti J, Rao SV. Robotic assisted percutaneous coronary intervention: hype or hope? J Am Heart Assoc. 2019.
29. Robotic-assisted percutaneous coronary intervention: rationale, implementation, case selection and limitations of current technology. J Clin Med. 2018.
30. Maor E, Eleid MF, Gulati R, Lerman A, Sandhu GS. Current and future use of robotic devices to perform percutaneous coronary interventions: a review. J Am Heart Assoc. 2017.
31. Siddamreddy S, Thotakura R, Dandu V, Kanuru S, Meegada S, et al. Corona Virus Disease 2019 (COVID-19) presenting as acute ST elevation myocardial infarction. Cureus. 2020.
32. Siripanthong B, Nazarian S, Muser D, Deo R, Santangeli P, Khanji MY, et al. Recognizing COVID-19–related myocarditis: The possible pathophysiology and proposed guideline for diagnosis and management. Heart Rhythm. 2020.
33. Al-Omar K, Bakkar S, Khasawneh L, Donatini G, Miccoli P, et al. Resuming elective surgery in the time of COVID-19: a safe and comprehensive strategy. Updates Surg. 2020.
34. Patel V, Jimenez E, Cornwell L, Tran T, Paniagua D, Denktas AE, et al. Cardiac surgery during the coronavirus disease 2019 pandemic: perioperative considerations and triage recommendations. 2020.
35. Rosenbaum L. The untold toll—The pandemic’s effects on patients without COVID-19. N Engl J Med. 2020.
36. Kutikov A, Weinberg DS, Edelman MJ, Horwitz EM, Uzzo RG, et al. A war on two fronts: cancer care in the time of COVID-19. Ann Intern Med. 2020;172.
37. Kwoh YS, Hou J, Jonckheere EA, Hayati S. A robot with improved absolute positioning accuracy for CT-guided stereotactic brain surgery. IEEE Trans Biomed Eng. 1988;35(2):153–60.
38. Santok GD, Rha KH. Robotic surgery: an evolution of future direction. Investig Clin Urol. 2016;57.
39. Zemmar A, Lozano AM, Nelson BJ. The rise of robots in surgical environments during COVID-19. Nat Mach Intell. 2020;2(10):566–72.
40. Candamourty R, Venkatachalam S, Ramesh Babu MR, Suresh Kumar G. Ludwig’s angina—an emergency: a case report with literature review. J Nat Sci Biol Med. 2012;3.
41. Wu Y, Wang F, Fan S, Chow JK. Robotics in dental implantology. Oral Maxillofac Surg Clin North Am. 2019.
42. Al Omran Y, Abdall-Razak A, Ghassemi N, Alomran S, Yang D, Ghanem AM. Robotics in cleft surgery: origins, current status, and future directions. Robot Surg Res Rev. 2019.
43. Dobbs TD, Cundy O, Samarendra H, Khan K, Whitaker IS. A systematic review of the role of robotics in plastic and reconstructive surgery—From inception to the future. Front Surg. 2017;4.
44. Khan ZH, Siddique A, Lee CW. Robotics utilization for healthcare digitization in global COVID-19 management. Int J Environ Res Public Health. 2020;17(11):3819.
45. Adamson AS, Smith A. Machine learning and healthcare disparities in dermatology. JAMA Dermatol. 2018;154:1247–8.
46. Langarizadeh M, Moghbeli F. Applying Naive Bayesian networks to disease prediction: systematic review. Acta Inform Med. 2016;24(5):364–9.
47. Stahl BC, Coeckelbergh M. Ethics of healthcare robotics: towards responsible research and innovation. Rob Auton Syst. 2016.
48. Su H, Hu Y, Li Z, Knoll A, Ferrigno G, De Momi E, et al. Reinforcement learning-based manipulation skill transferring for robot-assisted minimally invasive surgery. Proc IEEE Int Conf Robot Autom. 2020;2203–8.
49. Bach C, Miernik A, Schönthaler M. Training in robotics: the learning curve and contemporary concepts in training. Arab J Urol. 2014;12:58–61.
50. Mohamed K, Rodríguez-Román E, Rahmani F, Zhang H, Ivanovska M, Makka SA, et al. Borderless collaboration is needed for COVID-19—a disease that knows no borders. Infect Control Hosp Epidemiol. 2020;41.
51. Moradian N, Ochs HD, Sedikies C, Hamblin MR, Camargo CA, Martinez JA, et al. The urgent need for integrated science to fight COVID-19 pandemic and beyond. J Transl Med. 2020;18.
52. Chary M, Parikh S, Manini AF, Boyer EW, Radeos M. A review of natural language processing in medical education. West J Emerg Med. 2019.
53. Doan S, Bastarache L, Klimkowski S, Denny JC, Xu H. Integrating existing natural language processing tools for medication extraction from discharge summaries. J Am Med Inform Assoc. 2010;17(5):528–31.
54. Bajwa IS. Virtual telemedicine using natural language processing. Int J Inf Technol Web Eng. 2010.
55. Langarizadeh M, Moghbeli F. Applying Naive Bayesian networks to disease prediction: a systematic review. Acta Inform Med.
56. Lisboa PJG. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw. 2002;15.
57. Rossi L, Varriale A, Deleo G, Segramora VM. Machine learning and neural networks in vascular surgery. Eur J Vasc Endovasc Surg. 2019.
58. Tayeb S, Pirouz M, Sun J, Hall K, Chang A, Li J, et al. Toward predicting medical conditions using k-nearest neighbors. Proc IEEE Int Conf Big Data. 2017;3897–903.
59. Son YJ, Kim HG, Kim EH, Choi S, Lee SK, et al. Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc Inform Res. 2010.
| Files | ||
| Issue | Vol 7, No 3 (2024) | |
| Section | Review Article | |
| DOI | https://doi.org/10.18502/igj.v7i3.17875 | |
| Keywords | ||
| Artificial Intelligence COVID-19 SARS-CoV-2 Robotics | ||
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How to Cite
1.
Aziz K, Jabbari P. Artificial Intelligence and Robotics in Delivering Healthcare at the Time of COVID-19 Pandemic. Immunol Genet J. 2024;7(3):87-94.
