Rethinking physician–patient communication: exploring physicians’ use of AI in healthcare: a dual-factor perspective
DOI:
https://doi.org/10.1108/INMR-10-2025-0252Palavras-chave:
Artificial intelligence, Physician–patient communication, Use of health technology, UTAUT2, QSBResumo
PurposeThis study aims to identify the physicians’ intentions to use artificial intelligence (AI) in order to provide digital information to physicians about their patients by the Unified Theory of Acceptance and Use of Technology 2 and QSB theories.
Design/methodology/approachTo collect the necessary data, an electronic questionnaire was designed to survey 789 physicians working in Iraqi clinics and healthcare institutions. To analyze these data, a structural equation model was used using the AMOS.26 program.
FindingsThe results revealed the importance of the study model in using AI technology in communication between physicians and patients.
Research limitations/implicationsIt is concluded that communication between physicians and their patients should be based on the use of AI technology, as the vast majority of physicians surveyed supported this. This indicates the need to use AI technology and its digital tools in physicians’ performance of their healthcare tasks (consultations, examinations) and, consequently, in providing treatment to patients. Furthermore, this study is not without its limitations, namely its focus on AI. Thus, it should focus on other digital technologies.
Originality/valueThis research provides innovative value by bridging the digital transformation gap in digital healthcare systems for patients. It is the first study in Iraq to re-establish communication between physicians and their patients. This study explains the importance of using digital technologies, such as AI, in providing healthcare to patients from a dual-factor perspective (motivation and inhibition), thus contributing to bridge the emerging gaps in healthcare services provided to patients.
Downloads
Referências
Admassu, W., & Gorems, K. (2024). Analyzing health service employees’ intention to use e-health systems in southwest Ethiopia: Using UTAUT-2 model. BMC Health Services Research, 24(1), 1136. doi: https://doi.org/10.1186/s12913-024-11567-y.
Google ScholarCrossref PubMed
Ahmad, S. Z., & Khalid, K. (2017). The adoption of M-government services from the user’s perspectives: Empirical evidence from the United Arab Emirates. International Journal of Information Management, 37(5), 367–379. doi: https://doi.org/10.1016/j.ijinfomgt.2017.03.008.
Google ScholarCrossref
Ahmed, M. H., Bogale, A. D., Tilahun, B., Kalayou, M. H., Klein, J., Mengiste, S. A., & Endehabtu, B. F. (2020). Intention to use electronic medical record and its predictors among health care providers at referral hospitals, north-West Ethiopia, 2019: Using unified theory of acceptance and use technology 2 (UTAUT2) model. BMC Medical Informatics and Decision Making, 20(1), 207. doi: https://doi.org/10.1186/s12911-020-01222-x.
Google ScholarCrossref PubMed
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. doi: https://doi.org/10.1016/0749-5978(91)90020-t.
Google ScholarCrossref
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
Google Scholar
Akdur, G., Aydin, M. N., & Akdur, G. (2020). Adoption of mobile health apps in dietetic practice: Case study of diyetkolik. JMIR mHealth and uHealth, 8(10), e16911. doi: https://doi.org/10.2196/16911.
Google ScholarCrossref PubMed
Al-Shahrani, H. (2016). Investigating the determinants of mobile learning acceptance in higher education in Saudi Arabia. Northern Illinois University.
Google Scholar
Alazzam, M. B., Al-Sharo, Y. M., & Al-Azzam, M. K. (2018). Developing (UTAUT 2) model of adoption mobile health application in Jordan E-government. Journal of Theoretical and Applied Information Technology, 96(12), 3846–3860.
Google Scholar
Ali, S. A., Ali, S., & Shamim, S. (2026). Role of artificial intelligence in transforming diagnosis, treatment, and patient care: A review. Current Pharmacogenomics and Personalized Medicine, 23(1), E18756921404728. doi: https://doi.org/10.2174/0118756921404728251019182202.
Google ScholarCrossref
Al-Raeei, M. (2026). The applications of artificial intelligence in the diagnosis and treatment of diseases in soft tissue: From healthcare to future insights. Exploratory research and hypothesis. Medicine, 11(1).
Google Scholar
Alsamman, T. A., Alshaher, A. A., & Alsamman, A. T. A. (2022). An investigation the factors affecting towards adoption of digital Wallets in Iraq. In Digital Economy, Business Analytics, and Big Data Analytics Applications (pp. 237–256). Cham: Springer International Publishing.
Google ScholarCrossref
Alshaher, A. A., & Alhayawwi, Z. A. A. (2025). From novelty to necessity: Do Iraqis want to use generative artificial intelligence in their educational institutions?. In K. N. Atulya, J. Dharm Singh, M. Durgesh Kumar, & A. Joshi (Eds.), Intelligent Sustainable Systems. WorldS4 2025. Lecture Notes in Networks and Systems (Vol. 1732, pp. 113–126). Cham: Springer. doi: https://doi.org/10.1007/978-3-032-11524-9_9.
Google ScholarCrossref
Amina, N. J. (2024). The use of AI in enhancing patient communication. Research Invention Journal of Scientific and Experimental Sciences, 4(2), 45–48.
Google Scholar
Anderson, J. E., Schwager, P. H., & Kerns, R. L. (2006). The drivers for acceptance of tablet PCs by faculty in a college of business. Journal of Information Systems Education, 17(4), 429.
Google Scholar
Awad, A., Trenfield, S. J., Pollard, T. D., Ong, J. J., Elbadawi, M., McCoubrey, L. E., … Basit, A. W. (2021). Connected healthcare: Improving patient care using digital health technologies. Advanced Drug Delivery Reviews, 178, 113958. doi: https://doi.org/10.1016/j.addr.2021.113958.
Google ScholarCrossref PubMed
Berger, S. A., Håland, E., & Solbjør, M. (2026). Patient perspectives on trust in artificial intelligence–powered tools in Prostate Cancer diagnostics. Qualitative Health Research, 36(2-3), 276–288. doi: https://doi.org/10.1177/10497323251387545.
Google ScholarCrossref PubMed
Bogdanffy, R., Mundzic, A., Nymberg, P., Sundemo, D., Moberg, A., Wikberg, C., … Entezarjou, A. (2026). Exploring clinician perspectives on artificial intelligence in primary care: Qualitative systematic review and meta-synthesis. JMIR AI, 5, e72210. doi: https://doi.org/10.2196/72210.
Google ScholarCrossref PubMed
Candra, S., Frederica, E., Putri, H. A., & Loang, O. K. (2025). The UTAUT approach to Indonesia’s behavioral intention to use mobile health apps. Journal of Science and Technology Policy Management, 16(5), 892–911.
Google ScholarCrossref
Chan, K. Y., Gong, M., Xu, Y., & Thong, J. (2008). Examining user acceptance of SMS: An empirical study in China and Hong Kong. In PACIS 2008 Proceedings (Vol. 294).
Google Scholar
Chang, Y. T., Chao, C. M., Yu, C. W., & Lin, F. C. (2021). Extending the utility of UTAUT2 for hospital patients’ adoption of medical apps: Moderating effects of e-health literacyMobile Information Systems, 2021, 1–10. doi: https://doi.org/10.1155/2021/8882317.
Google ScholarCrossref
Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511–535. doi: https://doi.org/10.1016/s0022-4359(01)00056-2.
Google ScholarCrossref
Chong, A. Y. L., Darmawan, N., Ooi, K. B., & Lin, B. (2010). Adoption of 3G services among Malaysian consumers: An empirical analysis. International Journal of Mobile Communications, 8(2), 129–149. doi: https://doi.org/10.1504/ijmc.2010.031444.
Google ScholarCrossref
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. doi: https://doi.org/10.2307/249008.
Google ScholarCrossref
Davis, M., Dysart, G. C., Doupnik, S. K., Hamm, M. E., Schwartz, K. T., George-Milford, B., … Young, J. F. (2024). Adolescent, parent, and provider perceptions of a predictive algorithm to identify adolescent suicide risk in primary care. Academic Pediatrics, 24(4), 645–653. doi: https://doi.org/10.1016/j.acap.2023.12.015.
Google ScholarCrossref PubMed
De Witte, N. A., Rimando, C. R. D., Vandenborre, D., Aseron, A. M. C., Mojica, G. D., Damian, J. A., … Schraeyen, K. (2024). A culturally-sensitive approach to technology acceptance: The process of applying the UTAUT2 model in the Philippines. Procedia Computer Science, 248, 14–21. doi: https://doi.org/10.1016/j.procs.2024.10.358.
Google ScholarCrossref
Dimitrovski, T., Bath, P. A., Ketikidis, P., & Lazuras, L. (2021). Factors affecting general practitioners’ readiness to accept and use an electronic health record system in the republic of north Macedonia: A national survey of general practitioners. JMIR Medical Informatics, 9(4), e21109. doi: https://doi.org/10.2196/21109.
Google ScholarCrossref PubMed
Durak Yildiz, H. (2019). Examining the acceptance and use of online social networks by preservice teachers within the context of unified theory of acceptance and use of technology model. Journal of Computing in Higher Education, 31(1), 173–209.
Google ScholarCrossref
Eberi-Kalu, O. (2025), Do the motivational factors of artificial intelligence technology influence millennials more than members of Generation Z in online transactions?. International Journal of Advanced Nano Computing and Analytics, 4(1), June, pp. 36–54.
Google Scholar
Ekayanti, S. R. (2018). UTAUT in communication technology of learning management system. In 2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 253–258). IEEE.
Google ScholarCrossref
Faida, E. W., Supriyanto, S., Haksama, S., Suryaningtyas, W., Astuti, W., Nudji, B., & Hasina, S. N. (2022). The effect of performance expectancy and behavioral intention on the use of electronic medical record (EMR) in tertier hospital in Indonesia. International Journal of Health Sciences, 6(S9), 1195–1205. doi: https://doi.org/10.53730/ijhs.v6ns9.12729.
Google ScholarCrossref
Hahlweg, P., Bieber, C., Brütt, A. L., Dierks, M. L., Dirmaier, J., Donner-Banzhoff, N., … Härter, M. (2022). Moving towards patient-centered care and shared decision-making in Germany. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen, 171, 49–57. doi: https://doi.org/10.1016/j.zefq.2022.04.001.
Google ScholarCrossref
Haikal, F. A. R. I. Z., Tyas, N. K., Felcia, X. D., Oktavia, T. A. N. T. Y., & Sundaram, D. A. V. I. D. (2022). User acceptance analysis of the e-Health information system using UTAUT2 method. Journal of Theoretical and Applied Information Technology, 100(7), 2193–2206.
Google Scholar
Henseler, J., Ringle, C., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. doi: https://doi.org/10.1007/s11747-014-0403-8.
Google ScholarCrossref
Heudel, P. E., Crochet, H., & Blay, J. Y. (2024). Impact of artificial intelligence in transforming the doctor–cancer patient relationship. ESMO Real World Data and Digital Oncology, 3, 100026. doi: https://doi.org/10.1016/j.esmorw.2024.100026.
Google ScholarCrossref PubMed
Homburg, C., Wieseke, J., & Kuehnl, C. (2010). Social influence on salespeople’s adoption of sales technology: A multilevel analysis. Journal of the Academy of Marketing Science, 38(2), 159–168. doi: https://doi.org/10.1007/s11747-009-0157-x.
Google ScholarCrossref
Hong, S., Thong, J. Y., & Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819–1834. doi: https://doi.org/10.1016/j.dss.2006.03.009.
Google ScholarCrossref
Hsieh, P. J. (2016). An empirical investigation of patients’ acceptance and resistance toward the health cloud: The dual factor perspective. Computers in Human Behavior, 63, 959–969. doi: https://doi.org/10.1016/j.chb.2016.06.029.
Google ScholarCrossref
Huang, W., Ong, W. C., Wong, M. K. F., Ng, E. Y. K., Koh, T., Chandramouli, C., … Tromp, J. (2024). Applying the UTAUT2 framework to patients’ attitudes toward healthcare task shifting with artificial intelligence. BMC Health Services Research, 24(1), 455. doi: https://doi.org/10.1186/s12913-024-10861-z.
Google ScholarCrossref PubMed
Huang, H., Zhu, J., Chen, J., Qin, Y., & Chen, S. (2025). Exploring the factors influencing the adoption of online medical services by older adults: A modified UTAUT2 based study. Frontiers in Public Health, 13, 1559701. doi: https://doi.org/10.3389/fpubh.2025.1559701.
Google ScholarCrossref PubMed
Idana, R. F., Wardani, R., & Kusumawati, P. D. (2024). The role of readiness for change on behavioral intention viewed from performance and effort expectancy in the implementation of electronic medical records. Malahayati International Journal of Nursing and Health Science, 7(7), 845–856. doi: https://doi.org/10.33024/minh.v7i7.373.
Google ScholarCrossref
Irons, J. L., Duenser, A., Pickett, T., Haysom, G., & McGrath, M. J. (2026). AI in medical practice: Doctors’ perspective on the benefits, challenges and facilitators of artificial intelligence scribe use. Health and Technology, 16(3), 1–10. doi: https://doi.org/10.1007/s12553-025-01042-x.
Google ScholarCrossref
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic perspectives, 5(1), 193–206. doi: https://doi.org/10.1257/jep.5.1.193.
Google ScholarCrossref
Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404–416. doi: https://doi.org/10.1016/j.ijmedinf.2008.12.005.
Google ScholarCrossref PubMed
Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741–755. doi: https://doi.org/10.1287/mnsc.1040.0326.
Google ScholarCrossref
Ladan, M. A., Wharrad, H., & Windle, R. (2018). Towards understanding healthcare professionals’ adoption and use of technologies in clinical practice: Using Q-methodology and models of technology acceptance. BMJ Health and Care Informatics, 25(1), 27–37. doi: https://doi.org/10.14236/jhi.v25i1.965.
Google ScholarCrossref
Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14(1), 21–38. doi: https://doi.org/10.4301/s1807-17752017000100002.
Google ScholarCrossref
Lambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C. P., & Stephan, A. (2023). An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. npj Digital Medicine, 6(1), 111. doi: https://doi.org/10.1038/s41746-023-00852-5.
Google ScholarCrossref PubMed
Lee, Y. C. (2025). Rethinking artificial intelligence in medicine: From tools to agents. Clinical and Experimental Emergency Medicine, 12(2), 101–103. doi: https://doi.org/10.15441/ceem.25.125.
Google ScholarCrossref PubMed
Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance1. MIS Quarterly, 31(4), 705–737. doi: https://doi.org/10.2307/25148817.
Google ScholarCrossref
Lorenzini, G., Arbelaez Ossa, L., Shaw, D. M., & Elger, B. S. (2023). Artificial intelligence and the doctor–patient relationship expanding the paradigm of shared decision making. Bioethics, 37(5), 424–429. doi: https://doi.org/10.1111/bioe.13158.
Google ScholarCrossref PubMed
Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29–39. doi: https://doi.org/10.1016/j.chb.2008.06.002.
Google ScholarCrossref
Mesko, B., & Győrffy, Z. (2019). The rise of the empowered physician in the digital health era. Journal of Medical Internet Research, 21(3), e12490. doi: https://doi.org/10.2196/12490.
Google ScholarCrossref PubMed
Ngusie, H. S., Kassie, S. Y., Zemariam, A. B., Walle, A. D., Enyew, E. B., Kasaye, M. D., … Mengiste, S. A. (2024). Understanding the predictors of health professionals' intention to use electronic health record system: Extend and apply UTAUT3 model. BMC Health Services Research, 24(1), 889. doi: https://doi.org/10.1186/s12913-024-11378-1.
Google ScholarCrossref PubMed
Oberlechner, T. (2005). The psychology of the foreign exchange market. John Wiley & Sons.
Google Scholar
Ocaña-Fernández, Y., Valenzuela-Fernández, L. A., & Garro-Aburto, L. L. (2019). Artificial intelligence and its implications in higher education. Journal of Educational Psychology-Propositos y Representaciones, 7(2), 553–568.
Google Scholar
Pallamala, G., Jenkins, J., Sherin, P. K., Nyathi, A., & George, J. (2026). Healthcare professionals’ perspectives on artificial intelligence in clinical practice: A systematic review of facilitators and challenges. Oxford Open Digital Health, 4, oqag004. doi: https://doi.org/10.1093/oodh/oqag004.
Google ScholarCrossref PubMed
Patil Dilip, P., & Chafle, R. (2026). Fundamentals of predictive analytics in healthcare: Nephrological and pulmonological fundamentals of predictive analytics. Artificial Intelligence and Machine Learning in Neurology, 1, 283–313.
Google Scholar
Polites, G. L., & Karahanna, E. (2012). Shackled to the Status Quo: The inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance1. MIS Quarterly, 36(1), 21–42. doi: https://doi.org/10.2307/41410404.
Google ScholarCrossref
Rönkkö, M., & Cho, E. (2020). An updated guideline for assessing discriminant validity. Organizational Research Methods, 25(1), 6–14. doi: https://doi.org/10.1177/1094428120968614.
Google ScholarCrossref
Salim, B. (2012). An application of UTAUT model for acceptance of social media in Egypt: A statistical study. International Journal of Information Science, 2(6), 92–105. doi: https://doi.org/10.5923/j.ijis.20120206.05.
Google ScholarCrossref
Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty, 1(1), 7–59. doi: https://doi.org/10.1007/bf00055564.
Google ScholarCrossref
Sani, H. S., Mobaraki, M., Sholibor, A., Fallah, S., Abdi, F., Hasannezhad, M., & Jandaghian-Bidgoli, M. (2026). Adoption of artificial intelligence in primary health care: Systematic synthesis of stakeholder perspectives. BMC Primary Care, 27(1), 50. doi: https://doi.org/10.1186/s12875-025-03157-6.
Google ScholarCrossref PubMed
Silver, R. A., Subramaniam, C., & Stylianou, A. (2020). The impact of portal satisfaction on portal use and health-seeking behavior: Structural equation analysis. Journal of Medical Internet Research, 22(3), e16260. doi: https://doi.org/10.2196/16260.
Google ScholarCrossref PubMed
Singh, A. (2026). Artificial intelligence in healthcare data analytics: A comprehensive review of methods, applications, and challenges. Global Journal of STEM and Society, 1(1), 20–37.
Google Scholar
Sitthipon, T., Siripipatthanakul, S., Phayaphrom, B., Siripipattanakul, S., & Limna, P. (2022). Determinants of customers’ intention to use healthcare chatbots and apps in Bangkok, Thailand. International Journal of Behavioral Analytics, 2(2), 1–15.
Google Scholar
Tsai, J. M., Cheng, M. J., Tsai, H. H., Hung, S. W., & Chen, Y. L. (2019). Acceptance and resistance of telehealth: The perspective of dual-factor concepts in technology adoption. International Journal of Information Management, 49, 34–44. doi: https://doi.org/10.1016/j.ijinfomgt.2019.03.003.
Google ScholarCrossref
Türkeş, M. C., Stăncioiu, A. F., & Bănacu, C. S. (2024). The intention to use ChatGPT in office work in Romania: Between utility and hedonic motivation. Amfiteatru Economic, 26(67), 783–800. doi: https://doi.org/10.24818/ea/2024/67/783.
Google ScholarCrossref
Ungureanu, C., Jossart, A., Dadkhah, R., Bentakhou, E., Blaimont, M., Reiters, J. C., … Boukhris, M. (2026). The first institutional survey on implementing an artificial intelligence application for ECG diagnostics: Integration across medical specialties in routine clinical practice. Acta Cardiologica, 81, 1–9. doi: https://doi.org/10.1080/00015385.2025.2611607.
Google ScholarCrossref PubMed
Utomo, P., Kurniasari, F., & Purnamaningsih, P. (2021). The effects of performance expectancy, effort expectancy, facilitating condition, and habit on behavior intention in using mobile healthcare application. International Journal of Community Service and Engagement, 2(4), 183–197. doi: https://doi.org/10.47747/ijcse.v2i4.529.
Google ScholarCrossref
Van der Heijden, H. (2004). User acceptance of hedonic information systems1. MIS Quarterly, 28(4), 695–704. doi: https://doi.org/10.2307/25148660.
Google ScholarCrossref
Van Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers and Education, 50(3), 838–852. doi: https://doi.org/10.1016/j.compedu.2006.09.001.
Google ScholarCrossref
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. doi: https://doi.org/10.2307/30036540.
Google ScholarCrossref
Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation1. MIS Quarterly, 32(3), 483–502. doi: https://doi.org/10.2307/25148853.
Google ScholarCrossref
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. doi: https://doi.org/10.2307/41410412.
Google ScholarCrossref
Vidal-Silva, C., Sánchez-Ortiz, A., Serrano-Malebrán, J., Arriagada, V., Flores, M., Godoy, M., & Vargas, C. (2024). Social influence, performance expectancy, and price value as determinants of telemedicine services acceptance in Chile. Heliyon, 10(5), e27067. doi: https://doi.org/10.1016/j.heliyon.2024.e27067.
Google ScholarCrossref PubMed
Vijayakumar, S., Lee, V. V., Leong, Q. Y., Hong, S. J., Blasiak, A., & Ho, D. (2023). Physicians’ perspectives on AI in clinical decision support systems: Interview study of the CURATE. AI personalized dose optimization platform. JMIR Human Factors, 10, e48476. doi: https://doi.org/10.2196/48476.
Google ScholarCrossref PubMed
Weir, I. B., Stroud, A. M., Stout, J. J., Barry, B. A., Athreya, A. P., Bobo, W. V., & Sharp, R. R. (2025). Physician perspectives on the impact of artificial intelligence on the therapeutic relationship in mental health care: Qualitative study. JMIR Mental Health, 12(1), e81970. doi: https://doi.org/10.2196/81970.
Google ScholarCrossref PubMed
Wu, Y. L., Tao, Y. H., & Yang, P. C. (2008). The use of unified theory of acceptance and use of technology to confer the behavioral model of 3G mobile telecommunication users. Journal of Statistics and Management Systems, 11(5), 919–949.
Google ScholarCrossref
Xu, H., & Shuttleworth, K. M. J. (2025). Rethinking the replacement of physicians with AI. American Philosophical Quarterly, 62(1), 17–31. doi: https://doi.org/10.5406/21521123.62.1.02.
Google ScholarCrossref
Xu, L., Cherian, J., Alturise, F., Alkhalaf, S., Mikhaylov, A., & Comite, U. (2025). Artificial intelligence attitude and innovative performance in emerging economy: The mediating roles of AI supportive Autonomy, AI big data analytics. Journal of Global Information Management (JGIM), 33(1), 1–35.
Google Scholar
Xue, L., Yen, C. C., Chang, L., Chan, H. C., Tai, B. C., Tan, S. B., … Choolani, M. (2012). An exploratory study of ageing women's perception on access to health informatics via a mobile phone-based intervention. International Journal of Medical Informatics, 81(9), 637–648. doi: https://doi.org/10.1016/j.ijmedinf.2012.04.008.
Google ScholarCrossref PubMed
Yang, Y., Porntrakoon, P., & Li, J. (2024). Factors impacting people performance expectancy and behavioral intention with the Internet medical service in Chengdu, China. Local Administration Journal, 17(2), 129–154.
Google Scholar
Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731.
Google ScholarCrossref PubMed
Downloads
Publicado
Edição
Seção
Licença
Direitos autorais (c) 2026 Ali Alshaher, Marwan Ali
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
• O(s) autor(es) autoriza(m) a publicação do artigo na revista;
• O(s) autor(es) garante(m) que a contribuição é original e inédita e que não está em processo de avaliação em outra(s) revista(s);
• A revista não se responsabiliza pelas opiniões, ideias e conceitos emitidos nos textos, por serem de inteira responsabilidade de seu(s) autor(es);
• É reservado aos editores o direito de proceder ajustes textuais e de adequação do artigos às normas da publicação.
