A Systematic Review of the Factors Affecting the Artificial Intelligence Implementation in the Health Care Sector

  • Shaikha F. S. Alhashmi
  • Muhammad Alshurideh
  • Barween Al Kurdi
  • Said A. SalloumEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)


A systematic review for twenty-three research studies published between (2015–2018) was reviewed and analyzed deeply in order to answer the research question for the critical success factors for implementing artificial intelligence (AI) projects within the health sector. Afterwards, different constructs were elicited from the previous targeted studies to end up with a list of the most widely used external factors that are relied upon frequently during the process of adopting the technology. The study provide a critical discussion about what does (AI) means and how it can be linked to use the Technology Acceptance Model (TAM) to discuss the willingness to use (AI) systems by individuals.


Systematic review Artificial Intelligence (AI) Technology Acceptance Model (TAM) Health sector 



This work is a part of a dissertation submitted in fulfilment of MSc Informatics (Knowledge & Data Management) Faculty of Engineering & Information Technology At The British University in Dubai.


  1. 1.
    Fathema, N., Shannon, D., Ross, M.: Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) in higher education institutions. J. Online Learn. Teach. 11(2), 210–232 (2015)Google Scholar
  2. 2.
    Fayad, R., Paper, D.: The technology acceptance model e-commerce extension: a conceptual framework. Proc. Econ. Financ. 26, 1000–1006 (2015)CrossRefGoogle Scholar
  3. 3.
    Salloum, S.A., Al-Emran, M., Shaalan, K., Tarhini, A.: Factors affecting the E-learning acceptance: a case study from UAE. Educ. Inf. Technol. 24, 1–22 (2018)Google Scholar
  4. 4.
    Salloum, S.A., Shaalan, K.: Adoption of e-book for university students. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 481–494 (2018)Google Scholar
  5. 5.
    Salloum, S.A., Al-Emran, M., Khalaf, R., Habes, M., Shaalan, K.: An innovative study of e-payment systems adoption in higher education: theoretical constructs and empirical analysis. Int. J. Interact. Mob. Technol. 13(6), 68–83 (2019)CrossRefGoogle Scholar
  6. 6.
    Salloum, S.A., Al-Emran, M., Habes, M., Alghizzawi, M., Ghani, M.A., Shaalan, K.: Understanding the impact of social media practices on E-learning systems acceptance. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 360–369 (2019)Google Scholar
  7. 7.
    Who, X.: Extending TAM: success factors of mobile marketing. Am. Acad. Sch. Res. J. 1(1), 1–5 (2011)Google Scholar
  8. 8.
    Aldosari, B., Al-Mansour, S., Aldosari, H., Alanazi, A.: Assessment of factors influencing nurses acceptance of electronic medical record in a Saudi Arabia hospital. Inform. Med. Unlocked 10(December 2017), 82–88 (2018)CrossRefGoogle Scholar
  9. 9.
    Al-Adwan, A., Al-Adwan, A., Smedley, J.: Exploring students acceptance of e-learning using technology acceptance model in Jordanian universities. Int. J. Educ. Dev. Inf. Commun. Technol. 9(2), 4 (2013)Google Scholar
  10. 10.
    Bennani, A.-E., Oumlil, R.: The Acceptance of ICT by geriatricians reinforces the value of care for seniors in Morocco. IBIMA Publ. J. African Res. Bus. Technol. J. Afr. Res. Bus. Technol. 2014(2014), 1–10 (2014)Google Scholar
  11. 11.
    Nadri, H., Rahimi, B., Afshar, H.L., Samadbeik, M., Garavand, A.: Factors affecting acceptance of hospital information systems based on extended technology acceptance model: a case study in three paraclinical departments. Appl. Clin. Inform. 9(02), 238–247 (2018)CrossRefGoogle Scholar
  12. 12.
    Price, I.I., Nicholson, W.: Artificial intelligence in health care: applications and legal implications (2017)Google Scholar
  13. 13.
    Kononenko, I.: Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 23(1), 89–109 (2001)CrossRefGoogle Scholar
  14. 14.
    Shukla, S., Gupta, D.L., Prasad, B.R.: Comparative study of recent trends on cancer disease prediction using data mining techniques. Int. J. Database Theory Appl. 9(9), 107–118 (2016)CrossRefGoogle Scholar
  15. 15.
    Swarup: No title. Artif. Intell. Int. J. Comput. Corp. Res. 2(4) (2012)Google Scholar
  16. 16.
    Jie, W., Hai-yan, L., Biao, C., Yuan, Z.: Application of educational data mining on analysis of students’ online learning behavior. In: 2017 2nd International Conference onImage, Vision and Computing (ICIVC), pp. 1011–1015 (2017)Google Scholar
  17. 17.
    Lawrence, J., Palacios-González, D., Harris, C.: Artificial Intelligence. Cambridge Q. Healthc. Ethics 25(02), 250–261 (2016)CrossRefGoogle Scholar
  18. 18.
    Moen, H., et al.: Comparison of automatic summarisation methods for clinical free text notes. Artif. Intell. Med. 67, 25–37 (2016)CrossRefGoogle Scholar
  19. 19.
    Ziuziański, P., Furmankiewicz, M., Sołtysik-Piorunkiewicz, A.: E-health artificial intelligence system implementation: case study of knowledge management dashboard of epidemiological data in Poland. Int. J. Biol. Biomed. Eng. 8, 164–171 (2014)Google Scholar
  20. 20.
    Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Wallis, L., et al.: A roadmap for the implementation of mHealth innovations for image-based diagnostic support in clinical and public-health settings: a focus on front-line health workers and health-system organizations. Glob. Health Action 10(sup3), 1340254 (2017)CrossRefGoogle Scholar
  22. 22.
    Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1 (2015)CrossRefGoogle Scholar
  23. 23.
    Maruping, L.M., Bala, H., Venkatesh, V., Brown, S.A.: Going beyond intention: integrating behavioral expectation into the unified theory of acceptance and use of technology. J. Assoc. Inf. Sci. Technol. 68(3), 623–637 (2017)CrossRefGoogle Scholar
  24. 24.
    Alshurideh, D.M.: Do electronic loyalty programs still drive customer choice and repeat purchase behaviour? Int. J. Electron. Cust. Relatsh. Manag. 12(1), 40–57 (2019)Google Scholar
  25. 25.
    Mokyr, J.: The British Industrial Revolution: An Economic Perspective. Routledge, Abingdon (2018)CrossRefGoogle Scholar
  26. 26.
    Mijwel, M.M.: History of Artificial Intelligence. Comput. Sci. Coll. Sci. 1–6 (2015)Google Scholar
  27. 27.
    Authority, D.H.: No title (2018)Google Scholar
  28. 28.
    Albu, A., Stanciu, L.: Benefits of using artificial intelligence in medical predictions. In: 2015 E-Health and Bioengineering Conference (EHB), pp. 1–4 (2015)Google Scholar
  29. 29.
    Panicacci, S., Donati, M., Fanucci, L., Bellin, I., Profili, F., Francesconi, P.: Population health management exploiting machine learning algorithms to identify high-risk patients. In: 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), pp. 298–303 (2018)Google Scholar
  30. 30.
    Charleonnan, A., Fufaung, T., Niyomwong, T., Chokchueypattanakit, W., Suwannawach, S., Ninchawee, N.: Predictive analytics for chronic kidney disease using machine learning techniques. In: 2016 Management and Innovation Technology International Conference (MITicon), p. MIT-80 (2016)Google Scholar
  31. 31.
    Nithya, B., Ilango, V.:“Predictive analytics in health care using machine learning tools and techniques. In: 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 492–499 (2017)Google Scholar
  32. 32.
    Rajamhoana, S.P., Devi, C.A., Umamaheswari, K., Kiruba, R., Karunya, K., Deepika, R.: Analysis of neural networks based heart disease prediction system. In: 2018 11th International Conference on Human System Interaction (HSI), pp. 233–239 (2018)Google Scholar
  33. 33.
    Zhang, Q., Zhou, D., Zeng, X.: Hear the heart: daily cardiac health monitoring using Ear-ECG and machine learning. In: 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), pp. 448–451 (2017)Google Scholar
  34. 34.
    Yamada, Y., Kobayashi, M.: Detecting mental fatigue from eye-tracking data gathered while watching video: evaluation in younger and older adults. Artif. Intell. Med. (2018)Google Scholar
  35. 35.
    Library, I.X.D.: Cover art: customized web-based system for elderly people using elements of artificial intelligence [online]. Univ. Košice. IEEE Xplore Digit. Libr. (2019). Accessed 20 January 2019
  36. 36.
    Chen, D., Goyal, G., Go, R., Parikh, S., Ngufor, C.: Predicting time to first treatment in chronic lymphocytic leukemia using machine learning survival and classification methods. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp. 407–408 (2018)Google Scholar
  37. 37.
    Nibali, A., He, Z., Wollersheim, D.: Pulmonary nodule classification with deep residual networks. Int. J. Comput. Assist. Radiol. Surg. 12(10), 1799–1808 (2017)CrossRefGoogle Scholar
  38. 38.
    Fotin, S.V., Yin, Y., Haldankar, H., Hoffmeister, J.W., Periaswamy, S.: Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches. In: Medical Imaging 2016: Computer-Aided Diagnosis, vol. 9785, p. 97850X (2016)Google Scholar
  39. 39.
    Ertosun, M.G., Rubin, D.L.: Probabilistic visual search for masses within mammography images using deep learning. in: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1310–1315 (2015)Google Scholar
  40. 40.
    Shaikhina, T., Khovanova, N.A.: Handling limited datasets with neural networks in medical applications: a small-data approach. Artif. Intell. Med. 75, 51–63 (2017)CrossRefGoogle Scholar
  41. 41.
    Vemulapalli, V., et al.: Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data. Artif. Intell. Med. 74, 1–8 (2016)CrossRefGoogle Scholar
  42. 42.
    Baharom, F., Khorma, O.T., Mohd, H., Bashayreh, M.G.: Developing an extended technology acceptance model: doctors’ acceptance of electronic medical records in Jordan. In: ICOCI (2011)Google Scholar
  43. 43.
    Razali, N.M., Wah, Y.B.: Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. J. Stat. Model. Anal. 2(1), 21–33 (2011)Google Scholar
  44. 44.
    Marangunić, N., Granić, A.: Technology acceptance model: a literature review from 1986 to 2013. Univers. Access Inf. Soc. 14(1), 81–95 (2015)CrossRefGoogle Scholar
  45. 45.
    Al Dmour, H., Alshurideh, M., Shishan, F.: The influence of mobile application quality and attributes on the continuance intention of mobile shopping. Life Sci. J. 11(10), 172–181 (2014)Google Scholar
  46. 46.
    Alshurideh, M., Salloum, S. A., Al Kurdi, B., Al-Emran, M.: Factors affecting the social networks acceptance: an empirical study using PLS-SEM approach. In: 8th International Conference on Software and Computer Applications (2019)Google Scholar
  47. 47.
    Alshurideh, M., Al Kurdi, B., Salloum, S.A.: Examining the main mobile learning system drivers’ effects: a mix empirical examination of both the expectation-confirmation model (ECM) and the technology acceptance model (TAM). In: International Conference on Advanced Intelligent Systems and Informatics, pp. 406–417 (2019)Google Scholar
  48. 48.
    Alshurideh, M.T., Salloum, S.A., Al Kurdi, B., Monem, A.A., Shaalan, K.: Understanding the quality determinants that influence the intention to use the mobile learning platforms: a practical study. Int. J. Interact. Mob. Technol. 13(11), 157–183 (2019)CrossRefGoogle Scholar
  49. 49.
    Alshurideh, M.T., Shaltoni, A.M., Hijawi, D.S.: Marketing communications role in shaping consumer awareness of cause-related marketing campaigns. Int. J. Mark. Stud. 6(2), 163 (2014)Google Scholar
  50. 50.
    Briz-Ponce, L., García-Peñalvo, F.J.: An empirical assessment of a technology acceptance model for apps in medical education. J. Med. Syst. 39(11), 176 (2015)CrossRefGoogle Scholar
  51. 51.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)CrossRefGoogle Scholar
  52. 52.
    Altamony, H., Alshurideh, M., Obeidat, B.: Information systems for competitive advantage: implementation of an organisational strategic management process. In: Proceedings of the 18th IBIMA Conference on Innovation and Sustainable Economic Competitive Advantage: From Regional Development to World Economic, 9–10 May 2012, Istanbul, Turkey (2012)Google Scholar
  53. 53.
    Muk, A., Chung, C.: Applying the technology acceptance model in a two-country study of SMS advertising. J. Bus. Res. 68(1), 1–6 (2015)CrossRefGoogle Scholar
  54. 54.
    Teeroovengadum, V., Heeraman, N., Jugurnath, B.: Examining the antecedents of ICT adoption in education using an extended technology acceptance model (TAM). Int. J. Educ. Dev. ICT 13(3), 4–23 (2017)Google Scholar
  55. 55.
    Solano-Lorente, M., Martínez-Caro, E., Cegarra-Navarro, J.G.: Designing a framework to develop eloyalty for online healthcare services. Electron. J. Knowl. Manag. 11(1), 107–115 (2013)Google Scholar
  56. 56.
    Abu-Shanab, E., Al-Tarawneh, H.: The influence of social networks on high school students’ performance. Int. J. Web-Based Learn. Teach. Technol. 10(2), 49–59 (2015)CrossRefGoogle Scholar
  57. 57.
    Alloghani, M., Hussain, A., Al-Jumeily, D., Abuelma’atti, O.: Technology acceptance model for the use of M-health services among health related users in UAE. In: 2015 International Conference on Developments of E-Systems Engineering (DeSE), pp. 213–217 (2015)Google Scholar
  58. 58.
    Emad, H., El-Bakry, H.M., Asem, A.: A modified technology acceptance model for health informatics (2016)Google Scholar
  59. 59.
    Basak, E., Gumussoy, C.A., Calisir, F.: Examining the factors affecting PDA acceptance among physicians: an extended technology acceptance model. J. Healthc. Eng. 6(3), 399–418 (2015)CrossRefGoogle Scholar
  60. 60.
    Safdari, R., Saeedi, M.G., Valinejadi, A., Bouraghi, H., Shahnavazi, H.: Technology acceptance model in health care centers of Iran. Int. J. Comput. Sci. Netw. Secur. 17(1), 42 (2017)Google Scholar
  61. 61.
    Punnoose, A.C.: Determinants of intention to use eLearning based on the technology acceptance model. J. Inf. Technol. Educ. Res. 11(1), 301–337 (2012)Google Scholar
  62. 62.
    Wangpipatwong, S., Chutimaskul, W., Papasratorn, B.: Understanding citizen’s continuance intention to use e-government website: a composite view of technology acceptance model and computer self-efficacy. Electron. J. e-Govern. 6(1), 55–64 (2008)Google Scholar
  63. 63.
    Strudwick, G.: Predicting nurses’ use of healthcare technology using the technology acceptance model: an integrative review. CIN Comput. Inform. Nurs. 33(5), 189–198 (2015)CrossRefGoogle Scholar
  64. 64.
    Helia, V.N., Indira Asri, V., Kusrini, E., Miranda, S.: Modified technology acceptance model for hospital information system evaluation–a case study (2018)Google Scholar
  65. 65.
    San, A.N.C., Yee, C.J.: The modified technology acceptance model for private clinical physicians: a case study in Malaysia, Penang. Int. J. Acad. Res. Bus. Soc. Sci. 3(2), 380 (2013)Google Scholar
  66. 66.