Readiness for AI-Enabled Healthcare Systems: Insights from Healthcare Professionals
DOI:
https://doi.org/10.70749/ijbr.v4i1.2827Keywords:
Artificial Intelligence, AI-Enabled Healthcare Systems, Healthcare Professionals, Readiness Assessment, Attitudes toward AI, Clinical Decision Support, Digital Health, Healthcare Technology Adoption, Ethical Challenges, Medical Education.Abstract
Background: Artificial intelligence is a gradually advancing field that is being integrated into healthcare to help with clinical decision-making, improve patient safety, and operational efficiency. Even though AI-supported healthcare systems are becoming more popular, it is important to note that successful adoption of the technology depends significantly on the readiness of healthcare professionals who should apply these technologies in the clinics. To create a sustainable, efficient, and effective AI implementation into a healthcare environment, their awareness, attitudes, readiness, and perceived challenges need to be evaluated. Objective: This study was aimed at assessing the awareness, attitudes, preparedness and perceived challenges and future expectation of the healthcare professionals with regard to AI-enabled healthcare systems. Methods: The survey was a quantitative cross-sectional survey design. The sample population used in this research was 350 health care workers who were represented through medical doctors, nurses, pharmacists, allied health professionals, health care administrators. The data was collected with the help of a structured self-administered questionnaire comprising of demographic variables, knowledge regarding AI in healthcare, beliefs about AI-enabled systems, willingness to use AI, perceived barriers to implementation, and future expectations. Descriptive statistical analysis was carried out with frequencies and percentages and results were given in tables and figures. Results: The findings revealed a great awareness of the topic of artificial intelligence in healthcare, and the majority of the respondents were familiar with the concept of AI and clinical use. The reactions towards the healthcare systems that were AI-enabled were largely favorable, particularly regarding the improvement in the quality of the diagnoses and patient safety. Majority of the interviewees indicated that they would not hesitate to implement AI systems and accept the technological development. Organizational readiness, however, was described as moderate, and a smaller number of respondents indicated that their organizations were technologically ready to integrate AI. The key challenges that were identified were issues with data privacy, expensive implementation, ethical considerations, and insufficiency of technical expertise. Nevertheless, despite the difficulties, the respondents showed an optimistic attitude to the future of AI and gave strong support to the adoption of AI in medical education and the creation of clear regulations. Conclusion: It is concluded that healthcare providers have positive attitudes and intentions towards AI-enabled healthcare systems, yet effective application must be supported by better organizational preparedness, specific training opportunities, and strong ethical and regulatory actions. The presence of these factors is important to address in order to achieve the responsible and successful introduction of artificial intelligence to a healthcare practice.
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1. Jeyakumar, T., Younus, S., Zhang, M., Clare, M., Charow, R., Karsan, I., Dhalla, A., Al-Mouaswas, D., Scandiffio, J., Aling, J., Salhia, M., Lalani, N., Overholt, S., & Wiljer, D. (2023). Preparing for an artificial intelligence–enabled future: Patient perspectives on engagement and health care professional training for adopting artificial intelligence technologies in health care settings. JMIR AI, 2, e40973.
2. Jain, M., Goel, A., Sinha, S., & Dhir, S. (2021). Employability implications of artificial intelligence in healthcare ecosystem: Responding with readiness. foresight, 23(1), 73-94.
https://doi.org/10.1108/fs-04-2020-0038
3. Twaha, U. (2024). Mitigating Financial Waste in the US Healthcare System: An AI-Driven Framework for Real-Time Fraud Detection in Medicare and Medicaid Claims. Journal of Engineering and Computational Intelligence Review, 2(2), 71-81.
https://jecir.com/index.php/jecir/article/view/33
4. Kotp, M. H., Ismail, H. A., Basyouny, H. A., Aly, M. A., Hendy, A., Nashwan, A. J., Hendy, A., & Abd Elmoaty, A. E. (2025). Empowering nurse leaders: Readiness for AI integration and the perceived benefits of predictive analytics. BMC Nursing, 24(1).
https://doi.org/10.1186/s12912-024-02653-x
5. Nazir, Q. U. A. (2023). AI-Powered Radiology: Innovations and Challenges in Medical Imaging. Journal of Engineering and Computational Intelligence Review, 1(1), 7-13.
https://jecir.com/index.php/jecir/article/view/14
6. Noor, S. R., & Alim, I. (2023). Blockchain-Integrated ERP Platforms for Ensuring Security in US Financial Supply Chains. Journal of Business Insight and Innovation, 2(2), 107-119.
https://insightfuljournals.com/index.php/JBII/article/view/65
7. Wibowo, M. F., Pyle, A., Lim, E., Ohde, J. W., Liu, N., & Karlström, J. (2025). Insights into the current and future state of AI adoption within health systems in Southeast Asia: Cross-sectional qualitative study. Journal of Medical Internet Research, 27, e71591.
8. Hummelsberger, P., Koch, T. K., Rauh, S., Dorn, J., Lermer, E., Raue, M., Hudecek, M. F., Schicho, A., Colak, E., Ghassemi, M., & Gaube, S. (2023). Insights on the current state and future outlook of AI in health care: Expert interview study. JMIR AI, 2, e47353.
9. Alzghaibi, H. (2025). Nurses’ perspectives on AI-enabled wearable health technologies: Opportunities and challenges in clinical practice. BMC Nursing, 24(1).
https://doi.org/10.1186/s12912-025-03343-y
10. Cecil, J., Kleine, A., Lermer, E., & Gaube, S. (2025). Mental health practitioners’ perceptions and adoption intentions of AI-enabled technologies: An international mixed-methods study. BMC Health Services Research, 25(1).
https://doi.org/10.1186/s12913-025-12715-8
11. Rony, M. K., Parvin, M. R., & Ferdousi, S. (2023). Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future. Nursing Open, 11(1).
https://doi.org/10.1002/nop2.2070
12. Ankolekar, A., Eppings, L., Bottari, F., Pinho, I. F., Howard, K., Baker, R., Nan, Y., Xing, X., Walsh, S. L., Vos, W., Yang, G., & Lambin, P. (2024). Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness. Computational and Structural Biotechnology Journal, 24, 412-419.
https://doi.org/10.1016/j.csbj.2024.05.014
13. Urbi, S. R., & Tiva, M. G. (2025). Technology and innovation in healthcare: Adoption of AI and predictive analytics in hospital management. Pathfinder of Research, 3(2), 22-45.
https://doi.org/10.69937/pf.por.3.2.52
14. Elfaham, R. H., Alnaaim, S. A., Maqbul, M. S., Elfaham, S. H., Alharbi, G. A., Alshehri, W. A., Almahyawi, F. A., Aljadani, N. M., Bahkali, R. I., Eldeen Elbahaie, A. A., Shakrah, O. E., Alzubaidi, M. A., Alshehri, W. M., Alsultan, E. S., & Adam Alzahrani, A. T. (2025). The Human-AI frontier: An in-depth exploration of physicians' apprehension and outlook towards artificial intelligence in gastroenterology healthcare. Gastroenterology & Endoscopy, 3(4), 211-220.
https://doi.org/10.1016/j.gande.2025.05.001
15. Lee, A., Shankararaman, V., & Eng Lieh, O. (2024). Enhancing citizen service management through AI-enabled systems - a proposed AI readiness framework for the public sector. Research Handbook on Public Management and Artificial Intelligence, 79-96.
https://doi.org/10.4337/9781802207347.00014
16. Kamel Rahimi, A., Pienaar, O., Ghadimi, M., Canfell, O. J., Pole, J. D., Shrapnel, S., Van der Vegt, A. H., & Sullivan, C. (2024). Implementing AI in hospitals to achieve a learning health system: Systematic review of current enablers and barriers. Journal of Medical Internet Research, 26, e49655.
17. Hart, S. N., Day, P. L., & Garcia, C. A. (2025). Streamlining medical software development with CARE lifecycle and CARE agent: An AI-driven technology readiness level assessment tool. BMC Medical Informatics and Decision Making, 25(1).
https://doi.org/10.1186/s12911-025-03099-0
18. Rony, M. K., Kayesh, I., Bala, S. D., Akter, F., & Parvin, M. R. (2024). Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study. Heliyon, 10(4), e25718.
https://doi.org/10.1016/j.heliyon.2024.e25718
19. Movahed, M., & Bilderback, S. (2024). Evaluating the readiness of healthcare administration students to utilize AI for sustainable leadership: A survey study. Journal of Health Organization and Management, 38(4), 567-582.
https://doi.org/10.1108/jhom-12-2023-0385
20. Tun, H. M., Naing, L., Malik, O. A., & Rahman, H. A. (2025). Navigating ASEAN region artificial intelligence (AI) governance readiness in healthcare. Health Policy and Technology, 14(2), 100981.
https://doi.org/10.1016/j.hlpt.2025.100981
21. Bekbolatova, M., Mayer, J., Ong, C. W., & Toma, M. (2024). Transformative potential of AI in healthcare: Definitions, applications, and navigating the ethical landscape and public perspectives. Healthcare, 12(2), 125.
https://doi.org/10.3390/healthcare12020125I
22. Abdul, S., Adeghe, E. P., Adegoke, B. O., Adegoke, A. A., & Udedeh, E. H. (2024). AI-enhanced healthcare management during natural disasters: Conceptual insights. Engineering Science & Technology Journal, 5(5), 1794-1816.
https://doi.org/10.51594/estj.v5i5.1155
23. Ferede, D. (2025). Artificial intelligence (AI) and healthcare capabilities: A systematic literature review. F1000Research, 14, 20.
https://doi.org/10.12688/f1000research.158477.1
24. Kumar, P., Dwivedi, Y. K., & Anand, A. (2021). Responsible artificial intelligence (AI) for value formation and market performance in healthcare: The mediating role of patient’s cognitive engagement. Information Systems Frontiers, 25(6), 2197-2220.
https://doi.org/10.1007/s10796-021-10136-6
25. Haider, D. A., Aslam, D. A., Naseer, D. I., Shamim, R., Dr. Muhammad Ahtisham, & Manzoor, D. L. (2025). NORMATIVE VALUES OF FUNCTIONAL MOVEMENT SCREENING SCORE FMS TO PREDICT INJURY RISK AND LEVELOF FITNESS AMONG DOMESTIC CRICKET PLAYERS IN TWIN CITIES PAKISTAN. The Research of Medical Science Review, 3(9), 524–545.
https://medicalsciencereview.com/index.php/Journal/article/view/2124
26. Nawaz, D. S., Haider, D. H., Dr. Fatima Shahzadi, Mehmood, D. L., Shafiq, D. F., & Ashraf, D. H. (2025). SEVERITY OF HEADACHE WITH DIGITAL EYE STRAIN AMONG CALL CENTRE AGENTS DUE TO PROLONG SCREEN TIME AND ITS IMPACT ON WORK PRODUCTIVITY. Frontier in Medical and Health Research, 3(7), 111–131.
https://fmhr.net/index.php/fmhr/article/view/1036
27. Hysterosalpingograpy’s Role In Evaluating Tubal Pathologies Among Infertile Women Aged 20+ In Rawalpindi. (2025). The Research of Medical Science Review, 3(8), 1222-1242.
https://medicalsciencereview.com/index.php/Journal/article/view/2017
28. Haider, D. A., Mustafa, D. S., Arshad, D. N., Muhammad, D., Khalid, D. P., & Noor, D. R. (2025). PREVALENCE OF PLANTAR FASCIITIS AND ITS ASSOCIATION WITH HAMSTRING TIGHTNESS IN ADULTS. Frontier in Medical and Health Research, 3(6), 1532–1553.
https://fmhr.net/index.php/fmhr/article/view/988
29. Dr. Abdul Haseeb, Manzoor, D. A., Shakeel, D. M., Kanwal, D. Z., Malik, D. S., Ijaz, D. E., & Sardar, D. (2025). TO STUDY THE PREVALENCE OF CARPAL TUNNEL SYNDROME, ITS SEVERITY AND FUNCTIONAL STATUS AMONG HAIRDRESSERS IN TWIN CITIES OF PAKISTAN: A CROSS-SECTIONAL STUDY. Review Journal of Neurological & Medical Sciences Review, 3(4), 480–504.
https://nmsreview.org/index.php/rjnmsr/article/view/311
30. Sardar, D., Dr. Izza Sadozai, Javaid, D. A., Khalil, D. M., Dr. Ruqayya Bint-E-Ahmed, & Dr. Humna Ihsan. (2025). PATIENT AWARENESS AND PREFERENCES FOR PHYSIOTHERAPY TREATMENT OF ORTHOPEDIC CONDITIONS. A CROSS-SECTIONAL SURVEY OF PHYSIOTHERAPY AND PATIENT SATISFACTION WITH GIVEN TREATMENT. Frontier in Medical and Health Research, 3(6), 821–843.
https://fmhr.net/index.php/fmhr/article/view/865
31. El Arab, R. A., Alshakihs, A. H., Alabdulwahab, S. H., Almubarak, Y. S., Alkhalifah, S. S., Abdrbo, A., ... & Sagbakken, M. (2025). Artificial intelligence in nursing: a systematic review of attitudes, literacy, readiness, and adoption intentions among nursing students and practicing nurses. Frontiers in Digital Health, 7, 1666005.
https://doi.org/10.3389/fdgth.2025.1666005
32. Erkayıran, O., & Aslan, R. (2025). Evaluation of Nurses' Perceptions and Readiness for Artificial Intelligence Integration in Healthcare: A Cross‐Sectional Study in Turkey. Journal of Advanced Nursing.
https://doi.org/10.1111/jan.70256
33. Shiva, T. A., Ireen, N., & Islam, M. S. (2024). Optimizing Early Intervention Strategies for Neurodiverse Children (ASD): Reducing Long-Term Public Healthcare Costs through Parent-Mediated Training. Apex Journal of Social Sciences, 3(1), 30-52.
https://apexjss.com/index.php/AJSS/article/view/18
34. Kumar, R., Singh, A., Kassar, A. S. A., Humaida, M. I., Joshi, S., & Sharma, M. (2025). Adoption challenges to artificial intelligence literacy in public healthcare: an evidence based study in Saudi Arabia. Frontiers in Public Health, 13, 1558772.
https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1558772/abstract
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