Submitted Successfully!
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Ver. Summary Created by Modification Content Size Created at Operation
1 + 2542 word(s) 2542 2021-12-03 02:08:31 |
2 format correct Meta information modification 2542 2021-12-14 02:30:50 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Saigí-Rubió, F. People Feel about Robot-Assisted Surgery. Encyclopedia. Available online: (accessed on 08 December 2023).
Saigí-Rubió F. People Feel about Robot-Assisted Surgery. Encyclopedia. Available at: Accessed December 08, 2023.
Saigí-Rubió, Francesc. "People Feel about Robot-Assisted Surgery" Encyclopedia, (accessed December 08, 2023).
Saigí-Rubió, F.(2021, December 13). People Feel about Robot-Assisted Surgery. In Encyclopedia.
Saigí-Rubió, Francesc. "People Feel about Robot-Assisted Surgery." Encyclopedia. Web. 13 December, 2021.
People Feel about Robot-Assisted Surgery

The goal of the entry was to establish the factors that influence how people feel about having a medical operation performed on them by a robot. 

robot-assisted surgery (RAS) artificial intelligence (AI) technology acceptance model (TAM) logit regression Europe

1. Introduction

With advances in computing technology, artificial intelligence (AI) is becoming common, a higher-order family of applied knowledge capable of connecting lower-order technologies to generate innovations within economic and social systems [1][2]. AI devices can detect, capture, and analyse information and communicate data in real time, connecting with other technologies. Robots use AI to process and analyse data and to recognise and predict patterns. Indeed, AI could reshape medical care by improving both clinical and non-clinical applications [3][4][5][6][7][8][9][10]. In public health, AI could improve the early detection of sources of disease outbreaks [11], predict outcomes for critically ill patients, and predict adverse drug reactions [12].
In the healthcare sphere, different types of robots are used for a variety of purposes, including the early detection or treatment of a disease [13]; assistance for people with disabilities or cognitive issues to enable them to remain independent [14][15]; assistance for patients undergoing rehabilitation therapy [16]; the delivery of meals, medication and laundry in hospitals [17]; the provision of telemedicine services [18]; and the performance of surgery [19]. In the clinical sphere, robots are gradually being adopted to perform complicated operations, including minimally invasive surgery and guided non-surgical procedures. In a growing number of healthcare systems worldwide, robot-assisted surgery (RAS) is starting to be used. RAS is a minimally invasive technique capable of assisting surgeons with complicated surgical procedures [20][21][22][23].
Robots could absorb activities currently carried out by professionals [22][24], which would challenge traditional healthcare practices [23][25]. To what extent is it possible to foresee a near-future scenario in which minor routine surgery is directed by robots? And what are the patients’ or general public’s perceptions of having surgical procedures performed on them by robots, be it totally or partially? It is crucial to establish a robot strategy that is aligned with the objectives of the sector and its stakeholders. Without a patient-aligned strategy, any robot initiative is likely to remain at the pilot stages. So, knowing what the reasons are for people’s trust in or mistrust of robots being used in surgical interventions would represent a new contribution to the literature and would be useful for healthcare policy decision-making.
The available evidence shows that there is a whole series of advantages associated with RAS. These advantages include reduced risks and errors in surgical interventions, shorter recovery times and lower financial costs [26][27][28][29][30][31]. The vast majority of the evidence has been provided by healthcare professionals and not by potential patients. The little available evidence provided by patients highlights changes in patient care systems and quality, and in the configuration of medical teams [32][33][34]. However, there is hardly any evidence relating to trust in RAS. Indeed, it is on this particular aspect that our study makes a new contribution to the literature. In our inquiry into the factors determining citizens’ trust in RAS, we expand the spectrum of assessments, we complement the results obtained from professionals, and we provide evidence to enable public policy to strengthen the presence of RAS in those areas where it deems it appropriate to do so.

2. Research Contributions

By focusing our study on the analysis of trust that citizens (patients or future patients) have in RAS, we wanted to expand the scope of analysis to include a group of stakeholders that is not always taken into consideration when planning RAS strategies or public policies. In fact, and adding to the little available evidence from the user or patient perspective [32][33][34], our study is about establishing the factors that predict European citizens’ trust in RAS. Our ultimate intention has been to provide additional evidence so that public decision-makers or strategy designers can balance professionals’ positive perceptions against citizens’ reticence.
Based on the analysis of a large representative sample consisting of more than 27,901 citizens aged 15 years and over from 28 European countries in 2017, a model comprising the motivational, sociodemographic, and experience factors that predict trust in RAS was designed and tested. In general, the results obtained indicate that, as the experience of using robots increased, the predictive coefficients related to information, attitude and perception of robots became more negative. Furthermore, sociodemographic variables played an important predictive role. The effect of experience on trust in RAS was greater among men, people between 40 and 54 years old, and those with higher educational levels.
Health robotic could effectively perform tasks such as taking people’s temperature in public areas or at ports of entry, providing quarantined patients with support, and enabling virtual care. They could also be used to carry out many of the tasks deemed thankless, dirty or dangerous during the pandemic, such as decontamination, waste delivery and handling, or monitoring quarantine compliance [35][36]. Within this context, the majority of studies into the effects of RAS are based on analyses of healthcare professionals’ assessments, which generally indicate positive effects on surgical intervention risk reduction, efficiency, and quality, and on the minimisation and subsequent recovery of costs linked to such interventions [26][27][28][29][30][31].
However, the definitive implementation of robots in the healthcare sphere, with all the opportunities they offer and all the challenges they pose, will almost certainly result in the need to undertake a complete strategic overhaul of health services. Many obstacles still need to be overcome before the potential of robotics can be unleashed. One such obstacle is, without doubt, patients’ trust. It is known that patients’ trust is an important determinant of behaviours and experiences in both medical care and the doctor–patient relationship [37]. However, given its importance in surgical procedures, establishing trust should be a priority when faced with the possibility of new technologies such as RAS being integrated into surgical procedures. That is why it is important to understand how the characteristics of robotics affect patient’s trust, and what influences and leads to humans’ trust in robots when faced with the possibility of being operated on, autonomously, by a robot.
A patient’s intention and decision to have surgical procedures performed on him/her by robots, be it totally or partially, entail considerable implication and a high level of perceived risk on his/her part. Moreover, their adoption requires a longer process and more time. Once it has been understood how the characteristics of robotics influence a patient’s trust, it is then necessary to understand how important the dimensions of his/her trust are to the use of and support by robotics in the surgical sphere. Patients’ trust is not a singular, generalised phenomenon, but rather a series of nuanced relationships based on specific behaviours and expectations. Previous studies on robots being used in older people’s health management [38] or by service providers [39] have noted ambiguities in the definition of factors contributing to the establishment of trust, as well as the complexity of empirically isolating these factors [40].
Experience of robot use has a positive effect on trust, as do more positive attitudes towards robots (by increasing the degree of knowledge about their characteristics and benefits) [41]. However, if the focus were to be placed on the clinical setting and, in particular, on RAS, then prior expectations might lead to more negative feelings towards robots. Indeed, in our research, we contrasted the negative relationship between the majority of the predictors of ease of use, expected benefits, and information about, perception of, and attitude towards robots with trust in the use of robotics in a surgical intervention. In fact, the only non-sociodemographic predictive variable that seemed to have a positive relationship with trust in robots was prior experience of robot use. In other words, whereas all the motivational predictors relating to ex-ante information about robots had negative predictive power for trust, only ex-post experience, i.e., having previously used robots, generated trust. This important motivational limitation, which confines trust in robots solely to prior robot use in other spheres, is almost certainly due to the fact that the association between robots and the operating theatre is perceived as an extremely novel use of technology with potential risk or a very considerable need for cultural change. In fact, in research on predictors of use of all types of digital technologies, similar results can be found in perceived uses of such technologies in their early or preliminary stages [42][43][44][45].
Having identified the importance of prior experience of robot use, we analysed the predictors of trust for three different levels of experience (zero use, average use, and high use). The results indicated a clear substitution effect between ex-post experience and ex-ante perceptions. That is, as experience of robot use increased, the predictive coefficients relating to information about, attitude towards and perception of robots became more negative, as did the one relating to robots facilitating the performance of tasks. In other words, as prior experience of robot use increased, the more negative the effects of predictors linked to information about, attitudes towards and ex-ante perceptions of robot use were. This result, combined with the previous one, suggests that experience had a dual effect on trust. The first effect, or level effect, determined that prior experience of robot use was decisive for motivating trust in surgical interventions performed totally or partially by robots. The second effect, or marginal effect, determined that the greater the prior experience of robot use, the bigger the negative effects of predictors not linked to ex-ante experience. Experience of use generated trust, and at the same time, greater experience generated more mistrust of prior perceptions not linked to use.
The results of our research also determined that variables of a sociodemographic nature played an important predictive role. The results obtained for gender, age and educational level are particularly interesting. We performed a detailed analysis in all three cases. Regarding gender, for men, we found a higher positive incidence of experience, as well as higher predictive power (mistrust) of non-experiential variables linked to information about robots. For women, mistrust was based on a greater preponderance of perceptions and the anticipated facilitation of the performance of tasks. Although age usually leads to positive feelings towards robots [46], our results showed that, as age and, ultimately, experience of robots increased, age only had a significant positive impact on trust in the 40- to 54-year-old group. Meanwhile, the mistrust of non-experiential variables, especially those relating to information, perceptions and facilitation, reduced with age. Lastly, the analysis of educational level (years of completed education) also produced some interesting results. Firstly, we found that the predictive power of experience for trust in robots for surgical interventions increased with more years of education. Secondly, we found that the behaviour of experiential variables was more erratic. In short, we confirmed that the effect of experience on trust in robots for surgical interventions was higher among men, individuals aged between 40 and 54, and those who had higher educational levels. This sociodemographic characterisation could also be useful for the implementation of support policies for the robotisation of the health system.

3. Practical Implications

From the viewpoint of healthcare management and policy, our results suggest that the incentivisation of RAS should consider different motivational routes. To overcome the strong resistance to the implantation of robots in surgical interventions, it is highly recommended to take advantage of the positive synergies that prior use of this technology produces in spheres other than that of healthcare. The use of positive perceptions of surgical robotics held by strata of the population that already use robotics in their places of work or in the domestic setting is a good starting point for improving the situation. Meanwhile, healthcare management and policy could work on the entire set of negative perceptions of robotics held generally by the population that has never come into contact with robots. It is particularly important to consider the social implantation phase of the use of robotics in surgical practice, especially as citizens may see this technology as being in its early stages and risky, and as one that poses major cultural challenges.
So, despite the considerable—and more than proven—benefits that robot use can bring to a patient when performing a surgical intervention, it should be borne in mind that, when it comes to health, the patient is not entirely rational. The decision to have an operation usually entails high risk and uncertainty for the patient because it implies that he/she is placing his/her most precious ‘asset’ in the hands of a third party, without any indication—or guarantee—of what the outcome will be like. If, in addition, the operation is performed by an autonomous robot, i.e., without the surgeon’s assistance, the level of risk and uncertainty will increase, thus leading to a rise in stress levels. By parameterising the reasons that generate trust in and mistrust of robots, mainly by highlighting experience of use as a key element for generating trust, our research makes a new contribution to the state of the art and draws practical implications of robot use for healthcare policy and practice.
Beyond the importance of experience, the analysis of non-experiential motivations suggests that the availability of more and better information on the surgical procedure and on potential health outcomes will have a decisive impact on the patient’s trust and, ultimately, on the decision taken by him/her in this regard. On some occasions, this information can be obtained from indicators that provide evidence of the potential outcome, whereas on others, it can be obtained directly from the patient via research into his/her motivations. In our study, we found that some sociodemographic characterisations were more inclined towards trust in robot use for surgical practices. As the successes of robotics in medicine become more evident, it may require governments and funders to formulate distinct strategies aimed at groups that are more likely to trust in robots. However, given that the effect of experience on trust is twofold, i.e., first there is a level effect (greater experience of use equals more trust) and then a marginal effect (greater experience equals more mistrust of non-experiential motivations), it is important for public policy to take both aspects into account. To promote the level effect, research into this area needs increased funding, on the one hand to address regulatory, ethical, and legal issues, and, above all, the issue of liability. On the other, it is vital to produce more scientific evidence of the clinical efficacy and viability of this technology. Its standardisation may favour the spread of surgical skills in developing countries, via the Internet or via mobile platforms using telemedicine solutions that are well-controlled by AI-based algorithms [47]. Within the Horizon Europe research and innovation programme, the European Commission intends to create a new public–private partnership to join forces and ensure the coordination of AI, data, and robotics research and innovation (Action 5) [35].
According to Wehner et al. [48], it is a rule for a robot not to harm humans or allow humans to be harmed. Faced with a potential scenario in which the general public accepts that robotics will take potentially critical decisions [49], and in which the evolution of technology will lead to a reduction in production costs [50], the penetration of robotics in the surgical sphere could optimise the outcomes of and increase access to surgical care [44], as well as democratise surgical care and standardise surgical outcomes regardless of economic and geographical restraints [47][51].


  1. Bodrožić, Z.; Adler, P.S. The evolution of management models: A neo-Schumpeterian theory. Adm. Sci. Q. 2018, 63, 85–129.
  2. Trajtenberg, M. AI as the next GPT: A political-economy perspective. In The Economics of Artificial Intelligence: An Agenda; National Bureau of Economic Research (NBER) Working Paper (núm. 24245): Cambridge, MA, USA, 2018; pp. 175–186.
  3. Shaw, J.; Rudzicz, F.; Jamieson, T.; Goldfarb, A. Artificial Intelligence and the Implementation Challenge. J. Med Internet Res. 2019, 21, e13659.
  4. Yu, K.-H.; Beam, A.L.; Kohane, I.S. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2018, 2, 719–731.
  5. Shen, J.; Zhang, C.J.P.; Jiang, B.; Chen, J.; Song, J.; Liu, Z.; He, Z.; Krittanawong, C.; Fang, P.-H.; Ming, W.-K. Artificial intelligence versus clinicians in disease diagnosis: Systematic review. JMIR Med. Inform. 2019, 7, e10010.
  6. Medrano, I.H.; Guijarro, J.T.; Belda, C.; Ureña, A.; Salcedo, I.; Espinosa-Anke, L.; Saggion, H. Savana: Re-using electronic health records with artificial intelligence. Int. J. Interact. Multimed. Artif. Intell. 2018, 4, 1.
  7. Contreras, I.; Vehi, J. Artificial intelligence for diabetes management and decision support: Literature review. J. Med. Internet Res. 2018, 20, e10775.
  8. Topol, E.J. High-performance medicine: The convergence of human and artificial intelligence. Nat. Med. 2019, 25, 44–56.
  9. Jha, S.; Topol, E.J. Adapting to Artificial Intelligence: Radiologists and pathologists as information specialists. JAMA 2016, 316, 2353–2354.
  10. Naylor, C.D. On the Prospects for a (Deep) learning health care system. JAMA 2018, 320, 1099–1100.
  11. Acemoglu, D.; Restrepo, P. The wrong kind of AI? Artificial intelligence and the future of labor demand. Camb. J. Reg. Econ. Soc. 2019, 13, 25–35.
  12. Thiébaut, R.; Thiessard, F. Informatics artificial intelligence in public health and epidemiology. Yearb. Med. Inform. 2018, 27, 207–210.
  13. Thevenot, J.; Lopez, M.B.; Hadid, A. A survey on computer vision for assistive medical diagnosis from faces. IEEE J. Biomed. Health Inform. 2018, 22, 1497–1511.
  14. Leite, I.; Martinho, C.; Paiva, A. Social robots for long-term interaction: A survey. Int. J. Soc. Robot. 2013, 5, 291–308.
  15. Matarić, M.J.; Eriksson, J.; Feil-Seifer, D.J.; Winstein, C.J. Socially assistive robotics for post-stroke rehabilitation. J. Neuroeng. Rehabil. 2007, 4, 5.
  16. Krebs, H.; Palazzolo, J.; DiPietro, L.; Ferraro, M.; Krol, J.; Rannekleiv, K.; Volpe, B.; Hogan, N. Rehabilitation robotics: Performance-based progressive robot-assisted therapy. Auton. Robot 2003, 15, 7–20.
  17. Ichbiah, D. Robots: From science fiction to technological revolution. Choice Rev. Online 2005, 539, 544.
  18. Hou, C.; Jia, S.; Ye, G.; Takase, K. Switching remote robot manipulation in Internet TeleCare systems. Integr. Comput. Eng. 2004, 11, 227–238.
  19. Broadbent, E.; Stafford, R.; MacDonald, B. Acceptance of healthcare robots for the older population: Review and future directions. Int. J. Soc. Robot 2009, 1, 319–330.
  20. Kar, U.K. The Future of health and healthcare in a world of artificial intelligence. Arch. Biomed. Eng. Biotechnol. 2018, 1, 1–7.
  21. Kanevsky, J.; Corban, J.; Gaster, R.; Kanevsky, A.; Lin, S.; Gilardino, M. Big data and machine learning in plastic surgery: A new frontier in surgical innovation. Plast. Reconstr. Surg. 2016, 137, 890e–897e.
  22. Maeso, S.; Reza, M.; Mayol, J.; Blasco, J.A.; Guerra, M.; Andradas, E.; Plana, M.N. Efficacy of the da Vinci surgical system in abdominal surgery compared With that of laparoscopy. Ann. Surg. 2010, 252, 254–262.
  23. Ishikawa, N.; Watanabe, G.; Hirano, Y.; Inaki, N.; Kawachi, K.; Oda, M. Robotic dexterity: Evaluation of three-dimensional monitoring system and non-dominant hand maneuverability in robotic surgery. J. Robot Surg. 2007, 1, 231–233.
  24. Parish, J.M. The patient will see you now: The future of medicine is in your hands. J. Clin. Sleep Med. 2015, 11, 689–690.
  25. Shademan, A.; Decker, R.S.; Opfermann, J.D.; Leonard, S.; Krieger, A.; Kim, P.C.W. Supervised autonomous robotic soft tissue surgery. Sci. Transl. Med. 2016, 8, 337ra64.
  26. Guerra, F.; Pesi, B.; Bonapasta, S.A.; Perna, F.; Di Marino, M.; Annecchiarico, M.; Coratti, A. Does robotics improve minimally invasive rectal surgery? Functional and oncological implications. J. Dig. Dis. 2016, 17, 88–94.
  27. Ficarra, V.; Novara, G.; Ahlering, T.; Costello, A.; Eastham, J.A.; Graefen, M.; Guazzoni, G.F.; Menon, M.; Mottrie, A.; Patel, V.R.; et al. Systematic review and meta-analysis of studies reporting potency rates after robot-assisted radical prostatectomy. Eur. Urol. 2012, 62, 418–430.
  28. Jacobsen, M.F.; Konge, L.; Alberti, M.; La Cour, M.; Park, Y.S.; Thomsen, A.S.S. Robot-assisted vitreoretinal surgery improves surgical accuracy compared with manual surgery: A randomized trial in a simulated setting. Retina 2020, 40, 2091–2098.
  29. Khan, F.; Pearle, A.; Lightcap, C.; Boland, P.J.; Healey, J. Haptic Robot-assisted surgery improves accuracy of wide resection of bone tumors: A pilot study. Clin. Orthop. Relat. Res. 2013, 471, 851–859.
  30. Wallace, D.J.; Vardiman, A.B.; Booher, G.A.; Crawford, N.R.; Riggleman, J.R.; Greeley, S.L.; Ledonio, C.G. Navigated robotic assistance improves pedicle screw accuracy in minimally invasive surgery of the lumbosacral spine: 600 pedicle screws in a single institution. Int. J. Med. Robot Comput. Assist. Surg. 2020, 16, e2054.
  31. Ramsay, C.; Pickard, R.; Robertson, C.; Close, A.; Vale, L.; Armstrong, N.; A Barocas, D.; Eden, C.G.; Fraser, C.; Gurung, T.; et al. Systematic review and economic modelling of the relative clinical benefit and cost-effectiveness of laparoscopic surgery and robotic surgery for removal of the prostate in men with localised prostate cancer. Health Technol. Assess. 2012, 16, 1–313.
  32. Bailey, D.E.; Leonardi, P.M.; Barley, S.R. The lure of the virtual. Organ. Sci. 2012, 23, 1485–1504.
  33. Afkari, H.; Bednarik, R.; Mäkelä, S.; Eivazi, S. Mechanisms for maintaining situation awareness in the micro-neurosurgical operating room. Int. J. Hum.-Comput. Stud. 2016, 95, 1–14.
  34. Pelikan, H.R.M.; Cheatle, A.; Jung, M.F.; Jackson, S.J. Operating at a distance-how a teleoperated surgical robot reconfigures teamwork in the operating room. Proc. ACM Hum.-Comput. Interact. 2018, 2, 1–28.
  35. European Commission. White Paper on Artificial Intelligence—A European approach to excellence and trust (White Paper COM(2020) 65 final); European Commission: Brussels, Belgium, 2020; Available online: (accessed on 11 June 2020).
  36. Yang, G.-Z.; Nelson, B.J.; Murphy, R.R.; Choset, H.; Christensen, H.; Collins, S.H.; Dario, P.; Goldberg, K.; Ikuta, K.; Jacobstein, N.; et al. Combating COVID-19—The role of robotics in managing public health and infectious diseases. Sci. Robot 2020, 5, eabb5589.
  37. Chandra, S.; Mohaammadnezhad, M.; Ward, P. Trust and communication in a doctor-patient relationship: A literature review. J. Health Commun. 2018, 3, 36.
  38. Looije, R.; Neerincx, M.A.; Cnossen, F. Persuasive robotic assistant for health self-management of older adults: Design and evaluation of social behaviors. Int. J. Hum.-Comput. Stud. 2010, 68, 386–397.
  39. Lee, H.; Kim, J.; Kim, J. Determinants of success for application service provider: An empirical test in small businesses. Int. J. Hum.-Comput. Stud. 2007, 65, 796–815.
  40. Langer, A.; Feingold-Polak, R.; Mueller, O.; Kellmeyer, P.; Levy-Tzedek, S. Trust in socially assistive robots: Considerations for use in rehabilitation. Neurosci. Biobehav. Rev. 2019, 104, 231–239.
  41. Sanders, T.L.; MacArthur, K.; Volante, W.; Hancock, G.; MacGillivray, T.; Shugars, W.; Hancock, P.A. Trust and prior experience in human-robot interaction. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2017, 61, 1809–1813.
  42. Saigí-Rubió, F.; Torrent-Sellens, J.; Jiménez-Zarco, A. Drivers of telemedicine use: Comparative evidence from samples of Spanish, Colombian and Bolivian physicians. Implement. Sci. 2014, 9, 1–16.
  43. Gefen, D.; Karahanna, E.; Straub, D. Inexperience and experience with online stores: The importance of tam and trust. IEEE Trans. Eng. Manag. 2003, 50, 307–321.
  44. Dutton, W.H.; Shepherd, A. Trust in the Internet as an experience technology. Inf. Commun. Soc. 2006, 9, 433–451.
  45. Zhou, T. Examining mobile banking user adoption from the perspectives of trust and flow experience. Inf. Technol. Manag. 2012, 13, 27–37.
  46. Backonja, U.; Hall, A.K.; Painter, I.; Kneale, L.; Lazar, A.; Cakmak, M.; Thompson, H.J.; Demiris, G. Comfort and attitudes towards robots among young, middle-aged, and older adults: A cross-sectional study. J. Nurs. Sch. 2018, 50, 623–633.
  47. Panesar, S.; Cagle, Y.; Chander, D.; Morey, J.; Fernandez-Miranda, J.; Kliot, M. Artificial intelligence and the future of surgical robotics. Ann. Surg. 2019, 270, 223–226.
  48. Fitzgerald, D.J.; Whitesides, G.M.; Lewis, J.A.; Wood, R.J.; Wehner, M. An integrated design and fabrication strategy for entirely soft, autonomous robots. Nature 2016, 536, 451–455.
  49. Mirnezami, R.; Ahmed, A. Surgery 3.0, artificial intelligence and the next-generation surgeon. BJS 2018, 105, 463–465.
  50. Moore, G. Cramming more components onto integrated circuits. Proc. IEEE 1998, 86, 82–85.
  51. Panesar, S.S.; Ashkan, K. Surgery in space. BJS 2018, 105, 1234–1243.
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to :
View Times: 318
Revisions: 2 times (View History)
Update Date: 14 Dec 2021