Prospective Assessment Model of End Users for Artificial Intelligence Applications: A Systematic Review

Document Type : Review Article/ Systematic Review Article/ Meta Analysis

Authors

1 MSc of Medical Informatics, Health Information Technologies Unit of Economic Health Department, Mashhad University of Medical Sciences, Mashhad, Iran

2 Master of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran

3 Associate Professor of Biostatistics, Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Background: End user opinions are crucial for the success of health applications, particularly in the emerging field of artificial intelligence (AI) in medicine. Understanding end users' perspectives is essential for the acceptance and effectiveness of AI.

Objective: This systematic review aims to comprehensively analyze existing literature on end users' perspectives and acceptance models for AI applications. By synthesizing and critically evaluating research, this review seeks to identify key themes, methodologies, and knowledge gaps.

Methods: A systematic review was conducted in PubMed in 2023 to identify relevant peer-reviewed articles written in English. Inclusion criteria focused on original studies that validated assessment AI models from users' perspectives. Information extracted included publication details, countries of research, participant characteristics, data gathering and analysis methods, and attributes of the proposed models.

Results: Out of 3714 records, 19 papers were included in the study that were published between 2019 and 2022. Participants belonged to six categories: physicians, medical students, nurses, patients, and general public. The most important assessed factors in identified papers were “ethical issues, trust, and anxiety”, “usability”, “self-efficacy and knowledge”, “social”, “benefits”, “quality of the AI products and service support”, “AI acceptance, resistance of AI, attitude, and satisfaction” were explored. In addition, the commonly examined several moderating variables, including perceived ease of use, perceived usefulness, and perceived risks.

Conclusions: The findings contribute to understanding current trends and practices in end users' perspective research. Future studies should continue exploring end users' perspectives to enhance the development and implementation of effective AI systems in healthcare.

Keywords

References

1. Floridi, L., J. Cowls, M. Beltrametti, R. Chatila, P. Chazerand, V. Dignum, et al., An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Ethics, governance, and policies in artificial intelligence, 2021: p19-39. DOI: 10.1007/s11023-018-9482-5. [PubMed: 30930541].
https://doi.org/10.1007/s11023-018-9482-5
PMid:30930541 PMCid:PMC6404626
 
2. Wanner, J., L.-V. Herm, K. Heinrich and C. Janiesch, The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study. Electronic markets, 2022. 32(4): p. 2079-2102. DOI: 10.1007/s12525-022-00593-5.
https://doi.org/10.1007/s12525-022-00593-5
 
3. Lintz, J., Provider Satisfaction With Artificial Intelligence-Based Hand Hygiene Monitoring System During the COVID-19 Pandemic: Study of a Rural Medical Center. Journal of chiropractic medicine, 2023. DOI:10.1016/j.jcm.2023.03.004. [PubMed: 37360943].
https://doi.org/10.1016/j.jcm.2023.03.004
PMid:37360943 PMCid:PMC10040360
 
4. Santos, J.C., J.H.D. Wong, V. Pallath and K.H. Ng, The perceptions of medical physicists towards relevance and impact of artificial intelligence. Physical and engineering sciences in medicine, 2021. 44(3): p. 833-841. DOI:10.1007/s13246-021-01036-9. [PubMed: 34283393].
https://doi.org/10.1007/s13246-021-01036-9
PMid:34283393
 
5. Li, B., C. de Mestral, M. Mamdani and M. Al-Omran, Perceptions of Canadian vascular surgeons toward artificial intelligence and machine learning. Journal of vascular surgery cases and innovative techniques, 2022. 8(3): p. 466-472. DOI:10.1016/j.jvscit.2022.06.018. [PubMed: 36016703].
https://doi.org/10.1016/j.jvscit.2022.06.018
PMid:36016703 PMCid:PMC9396444
 
6. Yang, Y., J. Luo and T. Lan, An empirical assessment of a modified artificially intelligent device use acceptance model-From the task-oriented perspective. Frontiers in psychology, 2022. 13: p. 975307. DOI: 10.3389/fpsyg.2022.975307. [PubMed: 36017440].
https://doi.org/10.3389/fpsyg.2022.975307
PMid:36017440 PMCid:PMC9396124
 
7. Fujimori, R., K. Liu, S. Soeno, H. Naraba, K. Ogura, K. Hara, et al., Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence-Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation. JMIR formative research, 2022. 6(6): p. e36501. DOI: 10.2196/36501. [PubMed: 35699995].
https://doi.org/10.2196/36501
PMid:35699995 PMCid:PMC9237770
 
8. Grassini, S., Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence. Frontiers in psychology, 2023. 14: p. 1191628. DOI: 10.3389/fpsyg.2023.1191628 . [PubMed: 37554139].
https://doi.org/10.3389/fpsyg.2023.1191628
PMid:37554139 PMCid:PMC10406504
 
9. Jussupow, E., K. Spohrer and A. Heinzl, Identity Threats as a Reason for Resistance to Artificial Intelligence: Survey Study With Medical Students and Professionals. JMIR formative research, 2022. 6(3): p. e28750. DOI: 10.2196/28750 . [PubMed: 35319465].
https://doi.org/10.2196/28750
PMid:35319465 PMCid:PMC8987955
 
10. Iancu, I. and B. Iancu, Interacting with chatbots later in life: A technology acceptance perspective in COVID-19 pandemic situation. Frontiers in psychology, 2022. 13: p. 1111003. DOI: 10.3389/fpsyg.2022.1111003 . [PubMed: 36726494].
https://doi.org/10.3389/fpsyg.2022.1111003
PMid:36726494 PMCid:PMC9884968
 
11. Ye, T., J. Xue, M. He, J. Gu, H. Lin, B. Xu, et al., Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study. Journal of medical Internet research, 2019. 21(10): p. e14316. DOI: 10.2196/14316 . [PubMed: 31625950].
https://doi.org/10.2196/14316
PMid:31625950 PMCid:PMC6913088
 
12. Sisk, B.A., A.L. Antes, S. Burrous and J.M. DuBois, Parental Attitudes toward Artificial Intelligence-Driven Precision Medicine Technologies in Pediatric Healthcare. Children (Basel, Switzerland), 2020. 7(9).10.3390/children7090145. DOI: 10.3390/children7090145. PubMed: 32962204].
https://doi.org/10.3390/children7090145
PMid:32962204 PMCid:PMC7552627
 
13. Tran, A.Q., L.H. Nguyen, H.S.A. Nguyen, C.T. Nguyen, L.G. Vu, M. Zhang, et al., Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians. Frontiers in public health, 2021. 9: p. 755644. DOI: 10.3389/fpubh.2021.755644 . [PubMed: 34900904].
https://doi.org/10.3389/fpubh.2021.755644
PMid:34900904 PMCid:PMC8661093
 
14. Esmaeilzadeh, P., Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives. BMC medical informatics and decision making, 2020. 20(1): p. 170. DOI: 10.1186/s12911-020-01191-1 . [PubMed: 32698869].
https://doi.org/10.1186/s12911-020-01191-1
PMid:32698869 PMCid:PMC7376886
 
15. Uzir, M.U.H., H. Al Halbusi, R. Lim, I. Jerin, A.B. Abdul Hamid, T. Ramayah, et al., Applied Artificial Intelligence and user satisfaction: Smartwatch usage for healthcare in Bangladesh during COVID-19. Technol Soc, 2021. 67: p. 101780. DOI:10.1016/j.techsoc.2021.101780. [PubMed: 8528563].
https://doi.org/10.1016/j.techsoc.2021.101780
PMid:34697510 PMCid:PMC8528563
 
16. Aw, K.L., S. Suepiantham, A. Rodriguez, A. Bruce, S. Borooah and P. Cackett, Patients' Perception of Robot-Driven Technology in the Management of Retinal Diseases. Ophthalmology and therapy, 2023. DOI: 10.1007/s40123-023-00762-5 . [PubMed: 37369908].
https://doi.org/10.1007/s40123-023-00762-5
PMid:37369908 PMCid:PMC10442043
 
17. Huo, W., X. Yuan, X. Li, W. Luo, J. Xie and B. Shi, Increasing acceptance of medical AI: The role of medical staff participation in AI development. International journal of medical informatics, 2023. 175: p. 105073. DOI:1 10.1016/j.ijmedinf.2023.105073 . [PubMed: 37119693].
https://doi.org/10.1016/j.ijmedinf.2023.105073
PMid:37119693 PMCid:PMC10125218
 
18. Victor Mugabe, K., Barriers and facilitators to the adoption of artificial intelligence in radiation oncology: A New Zealand study. Technical innovations & patient support in radiation oncology, 2021. 18: p. 16-21. DOI: 10.1016/j.tipsro.2021.03.004 . [PubMed: 33981867].
https://doi.org/10.1016/j.tipsro.2021.03.004
PMid:33981867 PMCid:PMC8085695
 
19. Holdener, M., A. Gut and A. Angerer, Applicability of the User Engagement Scale to Mobile Health: A Survey-Based Quantitative Study. JMIR mHealth and uHealth, 2020. 8(1): p. e13244. DOI:10.2196/13244. [PubMed: 6969386].
https://doi.org/10.2196/13244
PMid:31899454 PMCid:PMC6969386
 
20. Pal, D., P. Roy, C. Arpnikanondt and H. Thapliyal, The effect of trust and its antecedents towards determining users' behavioral intention with voice-based consumer electronic devices. Heliyon, 2022. 8(4): p. e09271. DOI: 10.1016/j.heliyon.2022.e09271 . [PubMed: 35469331].
https://doi.org/10.1016/j.heliyon.2022.e09271
PMid:35469331 PMCid:PMC9034063
 
21. Zhai, H., X. Yang, J. Xue, C. Lavender, T. Ye, J.B. Li, et al., Radiation Oncologists' Perceptions of Adopting an Artificial Intelligence-Assisted Contouring Technology: Model Development and Questionnaire Study. Journal of medical Internet research, 2021. 23(9): p. e27122. DOI: 10.2196/27122 . [PubMed: 34591029].
https://doi.org/10.2196/27122
PMid:34591029 PMCid:PMC8517819
 
22. Li, X., M.Y. Jiang, M.S. Jong, X. Zhang and C.S. Chai, Understanding Medical Students' Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study. International journal of environmental research and public health, 2022. 19(14). DOI:110.3390/ijerph19148733 . [PubMed: 35886587].
https://doi.org/10.3390/ijerph19148733
PMid:35886587 PMCid:PMC9315694
 
23. Choudhury, A., Factors influencing clinicians' willingness to use an AI-based clinical decision support system. Frontiers in digital health, 2022. 4: p. 920662. DOI: 10.3389/fdgth.2022.920662 . [PubMed: 36339516].
https://doi.org/10.3389/fdgth.2022.920662
PMid:36339516 PMCid:PMC9628998
 
24. Kwak, Y., J.W. Ahn and Y.H. Seo, Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students' behavioral intentions. BMC nursing, 2022. 21(1): p. 267. DOI: 10.1186/s12912-022-01048-0. [PubMed: 36180902].
https://doi.org/10.1186/s12912-022-01048-0
PMid:36180902 PMCid:PMC9526272
 
25. Thurzo, A., W. Urbanová, B. Novák, L. Czako, T. Siebert, P. Stano, et al., Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel, Switzerland), 2022. 10(7). DOI: 10.3390/healthcare10071269 . [PubMed: 35885796].
https://doi.org/10.3390/healthcare10071269
PMid:35885796 PMCid:PMC9320442
 
26. Khanagar, S.B., A. Al-Ehaideb, P.C. Maganur, S. Vishwanathaiah, S. Patil, H.A. Baeshen, et al., Developments, application, and performance of artificial intelligence in dentistry - A systematic review. Journal of dental sciences, 2021. 16(1): p. 508-522. DOI: 10.1016/j.jds.2020.06.019 . [PubMed: 33384840].
https://doi.org/10.1016/j.jds.2020.06.019
PMid:33384840 PMCid:PMC7770297
 
27. Yin, J., K.Y. Ngiam and H.H. Teo, Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. Journal of medical Internet research, 2021. 23(4): p. e25759. DOI: 10.2196/25759 . [PubMed: 33885365].
https://doi.org/10.2196/25759
PMid:33885365 PMCid:PMC8103304
 
28. Ali, O., W. Abdelbaki, A. Shrestha, E. Elbasi, M.A.A. Alryalat and Y.K. Dwivedi, A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge, 2023. 8(1): p. 100333. DOI:10.1016/j.jik.2023.100333
https://doi.org/10.1016/j.jik.2023.100333
 
29. Sood, S.K., K.S. Rawat and D. Kumar, A visual review of artificial intelligence and Industry 4.0 in healthcare. Computers and Electrical Engineering, 2022. 101: p. 107948. DOI: 10.1016/j.compeleceng.2022.107948.
https://doi.org/10.1016/j.compeleceng.2022.107948
PMid:35495094 PMCid:PMC9040399
 
30. Gonzales, J.T., Implications of AI innovation on economic growth: a panel data study. Journal of Economic Structures, 2023: (1)12: p.13.
https://doi.org/10.1186/s40008-023-00307-w
 
31. Aoki, N., The importance of the assurance that "humans are still in the decision loop" for public trust in artificial intelligence: Evidence from an online experiment. Computers in human behavior, 2021. 114: p. 106572. DOI: 10.1016/j.chb.2020.106572.
https://doi.org/10.1016/j.chb.2020.106572
Razavi International Journal of Medicine
Volume 12, Issue 1
March 2024
Pages 1-9

Files

Share

How to cite

Statistics