The influence of AI-Driven Employee Performance Management (EPM) Systems in the sustained enhancement of Employee Mobilization, Organizational Performance, and the Change Culture in the healthcare sector: A scoping review analysis
Main Article Content
Abstract
Artificial Intelligence (AI) has caused massive changes in nature of workplaces in healthcare sector. Employee Performance Management (EPM) systems are undergoing a pivotal shift from annual manual data collection of personnel, characterized by subjective, bureaucratic, and demotivating methods into more agile human research operations. Modern methodological assessment emphasizes the importance of recording individual contribution in various areas, assessing not only the fulfillment and quality of assignments, but also aspects such as collaboration, creativity, innovative behavior and professional growth. The delivery of high-quality healthcare depends essentially on the effective functioning of personnel, who are the vital resource for maintaining reputation, fostering a culture of continuous improvement, and ensuring the overall effective operation of the healthcare sector. This scoping review adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines and encompassed 29 peer-reviewed empirical studies published from 2020 to 2025. AI-powered EPM platforms result in considerable improvements in efficiency, including increased frequent feedback, heightened employee engagement, identification of potential leaders and significant time savings for managers. Additionally, predictive analytics are vital in orchestrating healthcare organizations’ strategic and operational activities, assessing labor shortages, optimizing operations and managing high rates of burnout. Nevertheless, despite the acknowledged advantages of AI, there remain notable challenges to its implementation, including concerns about algorithmic bias, privacy, transparency, job displacement, organizational culture, and issues related to ethical and legal oversight. The analysis underscores the multidimensional nature of AI implementation in human resource management and how AI-driven EPM systems, mark a significant advance in accessing real-time performance data and provide considerable progression when utilized within appropriate guidelines. The successful integration of these systems relies on the synergy between AI technologies and human judgment, allowing healthcare organizations to cultivate a more dynamic, innovative and responsive workforce.
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References
I. Karaferis, D., Aletras, V., Raikou, M., & Niakas, D. Factors Influencing Motivation and Work Engagement of Healthcare Professionals. Materia socio-medica. 2022; 34(3), 216–224. https://doi.org/10.5455/msm.2022.34.216-224.
II. Endalamaw, A., Khatri, R., Erku, D., Nigatu, D., Zewdie, A., Wolka, E. & Assefa, Y. Successes and challenges towards improving quality of primary health care services: a scoping review. BMC Health Services Research. 2023; 23(1), 1–11. https://doi.org/10.1186/s12913-023-09917-3.
III. DeNisi, A., Murphy, K., Varma, A. and Budhwar, P. Performance management systems and multinational enterprises: where we are and where we should go. Human Resource Management. 2021; 60(5): 707-713. https://doi.org/10.1002/hrm.22080
IV. Siraj, N., & Hágen, I. Performance management system and its role for employee performance: Evidence from Ethiopian SMEs. Heliyon. 2023; 9(11), e21819. https://doi.org/10.1016/j.heliyon.2023.e21819.
V. Vainieri, M., Noto, G., Ferre, F., & Rosella, L. C. A Performance Management System in Healthcare for All Seasons?. Int J Environ Res Public Health. 2020; 17(15):5590. https://doi.org/10.3390/ijerph17155590
VI. Cavadi, G., & Cosenz, F. Enhancing healthcare resilience through AI-driven performance management systems. Journal of health organization and management. 2025; 1–17. Advance online publication. https://doi.org/10.1108/JHOM-05-2025-0261
VII. Karaferis, D., Balaska, D., Pollalis, Y. The Essential Importance of Virtual and Simulation Technologies in Modern Medical Education. Biostat Biom Open Access J. 2025; 12(2): 555832. https://doi.org/10.19080/BBOAJ.2025.12.555832.
VIII. Bhutoria, A. Personalized Education and Artificial Intelligence in the United States, China, and India: A Systematic Review Using a Human-in-The-Loop Model. Computers and Education: Artificial Intelligence. 2022; 3(21):100068. https://doi.org/10.1016/j.caeai.2022.100068
IX. Chuchu, B., Kyongo, J. Performance Management and Artificial Intelligence (AI): Enhancing Personalized Development with Continuous Feedback and Data-Driven Decisions. International Journal of Computer Science and Information Technology Research. 2025; 13(1), 13–22. https://doi.org/10.5281/zenodo.15025954
X. Karaferis, D., Balaska, D., Konstantakopoulou, D., Pollalis, Y. Long-Term Economic Consequences of Artificial Intelligence Implementation in Healthcare Institutional Performance Management, Effectiveness, and Patient Outcomes-The Importance of Thorough Health Technology Assessment: A Systematic Review. Biomed J Sci & Tech Res. 2025; 64(1), 56097-56108. https://doi.org/10.26717/BJSTR.2025.64.009977
XI. Varma, A., Pereira, V. E., & Patel, P. Artificial intelligence and performance management. Organizational Dynamics. 2024; 53(3):101037. https://doi.org/10.1016/j.orgdyn.2024.101037
XII. Maley, J.F., Dabić, M., Neher, A., Wuersch, L., Martin, L., Kiessling, T. Performance management in a rapidly changing world: implications for talent management. Management Decision. 2024; 62(10): 3085–3108, doi: https://doi.org/10.1108/MD-07-2023-1162
XIII. Gorski, A. and Dumitraşcu, D. D. Exploring the Dynamic Landscape of Performance Management: A Bibliometric Analysis of Emerging Trends. Studies in Business and Economics. 2023; 18(1): 342–366.
XIV. Bashynska, I., Mukhamejanuly, S., Malynovska, Y., Bortnikova, M., Saiensus,M., Malynovskyy, Y. Assessing the Outcomes of Digital Transformation Smartization Projects in Industrial Enterprises: A Model for Enabling Sustainability. Sustainability. 2023; 15(19):14075. https://doi.org/10.3390/su151914075
XV. Pandya, S. S., & Wang, J. Artificial intelligence in career development: a scoping review. Human Resource Development International. 2024; 27(3): 324–344. https://doi.org/10.1080/13678868.2024.2336881
XVI. Karaferis, D., Balaska, D., Pollalis, Y. Digitalization and Artificial Intelligence as Motivators for Healthcare Professionals. Japan Journal of Research. 2024; 6(3):1-9. https://doi.org/10.33425/2690-8077.1170
XVII. Nurlia, N., Daud, I., & Rosadi, M. E. AI implementation impact on workforce productivity: The role of AI training and organizational adaptation. Escalate: Economics and Business Journal. 2023; 1(01), 01-13. https://doi.org/10.61536/escalate.v1i01.6
XVIII. Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science. 2023; 26, 39-68. https://doi.org/10.28945/5078
XIX. van Elten, H. J., van der Kolk, B., & Sülz, S. Do different uses of performance measurement systems in hospitals yield different outcomes? Health care management review. 2021; 46(3), 217–226. https://doi.org/10.1097/HMR.0000000000000261
XX. van der Kolk, B. Performance measurement in the public sector: Mapping
years of survey research. Financial Accountability & Management. 2022; 38, 703–729.
https://doi.org/10.1111/faam.12345
XXI. Levac, D., Colquhoun, H., O'Brien, K.K. Scoping studies: advancing the methodology. Implementation Sci. 2010; 5:69. https://doi.org/10.1186/1748-5908-5-69
XXII. Arksey, H., O'Malley, L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005; 8:19–32. https://doi.org/10.1080/1364557032000119616
XXIII. Peters, MDJ., Marnie, C., Tricco, A.C., Pollock, D., Munn, Z., Alexander, L., et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Implement. 2021; 19:3–10. https://doi.org/10.1097/XEB.0000000000000277
XXIV. Tricco, A.C., Lillie, E., Zarin, W., O'Brien, K.K., Colquhoun, H., Levac, D., et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018; 169:467–73. https://doi.org/10.7326/M18-0850
XXV. Vidè F., Micacchi L., Barbieri M., Valotti G. The renaissance of performance appraisal: Engaging public employees through perceived developmental purpose and justice. Review of Public Personnel Administration. 2022; 43(4), 623–651. https://doi.org/10.1177/0734371X221116584
XXVI. Aguinis, H., & Burgi-Tian, J. Measuring performance during crises and beyond: The Performance Promoter Score. Business horizons. 2021; 64(1), 149–160. https://doi.org/10.1016/j.bushor.2020.09.001
XXVII. Zhang, H. Exploring the Impact of AI on Human Resource Management: A Case Study of Organizational Adaptation and Employee Dynamics. IEEE Transactions on Engineering Management. 2024; (71):14991-15004. https://doi.org/10.1109/TEM.2024.3457520
XXVIII. Noto, G. A Pragmatic and Systemic Approach to Advance Research in Health Policy and Management Comment on "Insights Gained From a Re-analysis of Five Improvement Cases in Healthcare Integrating System Dynamics Into Action Research". Int J Health Policy Manag. 2023; 12:7690. https://doi.org/10.34172/ijhpm.2022.7690
XXIX. Noto, G., De Domenico, F., Buttigieg, S. C., & Barresi, G. Managing and measuring performance of health prevention services: a simulation-based approach. Journal of health organization and management. 2025; 39(9), 305–324. https://doi.org/10.1108/JHOM-10-2024-0422
XXX. Thusi, X. Performance Management Systems (PMS): Challenges and Opportunities in the Public Sector. International Journal of Social Science Research and Review. 2023; 6(6):19-31. https://doi.org/10.47814/ijssrr.v6i6.1251
XXXI. de Melo Santos, C. J., Barbosa, A. S., & Sant'Anna, Â. M. O. Performance measurement systems in primary health care: a systematic literature review. BMC health services research. 2025; 25(1), 353. https://doi.org/10.1186/s12913-025-12412-6
XXXII. Karaferis, D., Balaska, D., Pollalis, Y. Design and Development of Data-Driven AI to Reduce the Discrepancies in Healthcare EHR Utilization. J Clin Med Re. 2025; 5(1):100184. https://doi.org/10.71010/AJCMR.2025-e184
XXXIII. Gartner, J. B., & Lemaire, C. Dimensions of performance and related key performance indicators addressed in healthcare organisations: A literature review. The International journal of health planning and management. 2022; 37(4), 1941–1952. https://doi.org/10.1002/hpm.3452
XXXIV. Garengo, P., & Betto, F. The role of organisational culture and leadership style in performance measurement and management: a longitudinal case study. Production Planning & Control. 2024; 35(2): 151–169. doi:10.1080/09537287.2022.2058431
XXXV. Homauni, A., Markazi-Moghaddam, N., Mosadeghkhah, A., Noori, M., Abbasiyan, K., & Jame, S. Z. B. Budgeting in Healthcare Systems and Organizations: A Systematic Review. Iranian journal of public health. 2023; 52(9), 1889–1901. https://doi.org/10.18502/ijph.v52i9.13571
XXXVI. Karaferis, D., Balaska, D., Lavrentiadis, V., & Pollalis, Y. Leveraging Artificial Intelligence and Diverse Strategies to Alleviate Burnout and Optimize Workload Management for Nursing Personnel to Enhance Performance: A Structured Literature Review. Ameri J Clin Med Re. 2025; AJCMR-212. doi:10.71010/AJCMR.2025-e212
XXXVII. Carr, M., & Beck, M. P. Clinician responses to management control: Case evidence from a university hospital during the fiscal crisis. Financial Accountability & Management. 2020; 36(3): 319-337. https://doi.org/10.1111/faam.12224
XXXVIII. Karaferis, D., Balaska, D., Karaferi, M. E., & Pollalis, Y. Assessment of Knowledge and Attitudes of Healthcare Personnel Towards Artificial Intelligence Technologies in Greece: A Survey Study. Hygiene. 2025; 5(4), 44. https://doi.org/10.3390/hygiene5040044
XXXIX. de Las Heras-Rosas, C., Herrera, J., & Rodríguez-Fernández, M. Organisational Commitment in Healthcare Systems: A Bibliometric Analysis. International journal of environmental research and public health. 2021; 18(5), 2271. https://doi.org/10.3390/ijerph18052271
XL. He, L., Wall, D., Reeck, C., & Bhatia, S. Information acquisition and decision strategies in intertemporal choice. Cognitive Psychology. 2023; 142, 101562. https://doi.org/10.1016/j.cogpsych.2023.101562
XLI. de Menezes, L.M., Escrig-Tena, A.B. Performance measurement systems in the health and care sector: Are targets and monitoring additional demands or resources for employees? International Journal of Operations & Production Management. 2023; 43(13): 302–329. https://doi.org/10.1108/IJOPM-12-2022-0763
XLII. Swain, S., Muduli, K., Kumar, A., & Luthra, S. Analysis of barriers of mHealth adoption in the context of sustainable operational practices in health care supply chains. International Journal of Industrial Engineering and Operations Management. 2024; 6(2), 85-116.
XLIII. Chilunjika, A., Intauno, K., & Chilunjika, S. R. Artificial intelligence and public sector human resource management in South Africa: Opportunities, challenges and prospects. SA Journal of Human Resource Management. 2022; 20, 1972. https://doi.org/10.4102/sajhrm.v20i0.1972
XLIV. Sreenivasan, A., Suresh, M. Future of healthcare start-ups in the era of digitalization: bibliometric analysis. International Journal of Industrial Engineering and Operations Management. 2022; 4(1-2), 1–18. https://doi.org/10.1108/IJIEOM-10-2022-0046
XLV. Panda, G., Dash, M.K., Samadhiya, A., Kumar, A., Mulat-weldemeskel, E. Artificial intelligence as an enabler for achieving human resource resiliency: past literature, present debate and future research directions. International Journal of Industrial Engineering and Operations Management. 2024; 6(4): 326–347. https://doi.org/10.1108/IJIEOM-05-2023-0047
XLVI. Marçal, D., Metrôlho, J., & Ribeiro, F. Integrating Sentiment Analysis into Agile Feedback Loops for Continuous Improvement. Applied Sciences. 2025; 15(22), 12329. https://doi.org/10.3390/app152212329
XLVII. Giamos, D., Doucet, O., & Léger, P. M. Continuous Performance Feedback: Investigating the Effects of Feedback Content and Feedback Sources on Performance, Motivation to Improve Performance and Task Engagement. Journal of Organizational Behavior Management. 2024; 44(3), 194–213. https://doi.org/10.1080/01608061.2023.2238029
XLVIII. Pant, I., Patro, L., Sedlander, E., Chandrana, S., & Rimal, R. Monitor to innovate with feedback loops: process evaluation protocol for an anemia prevention intervention. Gates open research. 2022; 6, 13. https://doi.org/10.12688/gatesopenres.13417.2
XLIX. Yadav, M., Kakkar, M., & Kaushik, P. Harnessing Artificial Intelligence to Empower HR Processes and Drive Enhanced Efficiency in the Workplace to Boost Productivity. International Journal on Recent and Innovation Trends in Computing and Communication. 2023; 11(8s), 381-390. https://doi.org/10.17762/ijritcc.v11i8s.7218
L. Votto, A. M., Valecha, R., Najafirad, P., & Rao, H. R. Artificial intelligence in tactical human resource management: A systematic literature review. International Journal of Information Management Data Insights. 2021; 1(2), 100047. https://doi.org/10.1016/j.jjimei.2021.100047
LI. Helanka De Silva, W.R., & Athuraliya, B. Enhanced Employee Performance Management System with AI Driven Analytics. 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). 2025; 1-6. doi:10.1109/ICDSAAI65575.2025.11011577
LII. Olan, F., Arakpogun, E. O., Suklan, J., Nakpodia, F., Damij, N., & Jayawickrama, U. Artificial intelligence and knowledge sharing: Contributing factors to organizational performance. Journal of Business Research. 2022; 145, 605-615. doi:10.1016/j.jbusres.2022.03.008
LIII. Feldkamp, T., Langer, M., Wies, L., & König, C. J. Justice, trust, and moral judgements when personnel selection is supported by algorithms. European Journal of Work and Organizational Psychology. 2024; 33(2), 130-145. doi:10.1080/1359432X.2023.2169140
LIV. Oravec, J. A. The emergence of “truth machines”?: Artificial intelligence approaches to lie detection. Ethics Inf Technol. 2022; 24(1), 6. https://doi.org/10.1007/s10676-022-09621-6
LV. Varsha, P.S. How can we manage biases in artificial intelligence systems–A systematic literature review. International Journal of Information Management Data Insights. 2023; 3(1), 100165. https://doi.org/10.1016/j.jjimei.2023.100165
LVI. Mollah, M. A., Rana, M., Amin, M. B., Sony, M. M. A. A. M., Rahaman, M. A., & Fenyves, V. Examining the Role of AI-Augmented HRM for Sustainable Performance: Key Determinants for Digital Culture and Organizational Strategy. Sustainability. 2024; 16(24), 10843. https://doi.org/10.3390/su162410843
LVII. Agarwal, P., Swami, S., and Malhotra, S. K. Artificial intelligence adoption in the post COVID-19 new-normal and role of smart technologies in transforming business: a review. J. Sci. Technol. Policy Manag. 2022; https://doi.org/10.1108/JSTPM-08-2021-0122
LVIII. Tatineni, S., & Allam, K. AI-driven continuous feedback mechanisms in DevOps for proactive performance optimization and user experience enhancement in software development. Journal of AI in Healthcare and Medicine. 2024; 4(1), 114-151. https: //healthsciencepub.com/index.php/jaihm/article/view/75
LIX. López-Cabarcos, M. Á., Vázquez-Rodríguez, P., & Quiñoá-Piñeiro, L. M. An approach to employees' job performance through work environmental variables and leadership behaviours. Journal of Business Research. 2022; 140, 361-369. https://doi.org/10.1016/j.jbusres.2021.11.006
LX. Yan, B., Yao, B., Li, Q., & Dong, Q. Study on the impact of supply chain dynamic capabilities on long-term performance of enterprises. Sustainability. 2022; 14(19), 12441.
LXI. Colombari, R., Geuna, A., Helper, S., Martins, R., Paolucci, E., Ricci, R., & Seamans, R. The interplay between data-driven decision-making and digitalization: A firm-level survey of the Italian and US automotive industries. International Journal of Production Economics. 2023; 255, 108718.
LXII. Alnsour, Y., Johnson, M., Albizri, A., & Harfouche, A. H. Predicting patient length of stay using artificial intelligence to assist healthcare professionals in resource planning and scheduling decisions. Journal of Global Information Management (JGIM). 2023; 31(1), 1-14. https://doi.org/10.4018/JGIM.323059
LXIII. Goyal, M., Verma, K., & Kapoor, R. The impact of AI-powered coaching assistants in employee development. International Journal of HR Tech. 2023; 15(2), 22-39.
LXIV. Bhatia, R., & Dutta, S. AI in HR: The role of adaptive learning and personalization. Journal of Human Capital Development. 2020; 12(3), 45-61.
LXV. Balaska, D., Karaferis, D., Konstantakopoulou, D., Pollalis, Y. Sustainable Digital Healthcare Technologies: Exploring Further Possibilities. Biomed J Sci & Tech Res. 2025; 64(1), 56097-56107. https://doi.org/10.26717/BJSTR.2025.64.009976
LXVI. Koebe, P. How digital technologies and AI contribute to achieving the health-related SDGs, International Journal of Information Management Data Insights. 2025; 5(1), 100298. https://doi.org/10.1016/j.jjimei.2024.100298
LXVII. Caci, L., Nyantakyi, E., Blum, K., Sonpar, A., Schultes, M. T., Albers, B., & Clack, L. Organizational readiness for change: A systematic review of the healthcare literature. Implementation research and practice. 2025; 6, 26334895251334536. https://doi.org/10.1177/26334895251334536
LXVIII. Jo, Y., & Hong, A. J. Development and Validation of a Readiness for Organizational Change Scale. Sage Open. 2023; 13(4). https://doi.org/10.1177/21582440231207705
LXIX. Lim, H., Sumner, J., Ang, A., Keck, C., Chew, E., Yip, A. Organizational Readiness for Change in the Era of Smart Hospital Wards: Qualitative Study of Health Care Workers’ Insights. JMIR Hum Factors. 2025; 12, e81932. https://humanfactors.jmir.org/2025/1/e81932
LXX. Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon. 2024; 10(4), e26297. https://doi.org/10.1016/j.heliyon.2024.e26297.
LXXI. Karaferis, D., Balaska, D., Pollalis, Y. Artificial Intelligence and Robotics: Catalysts or Threats in the Development of Healthcare. Biostat Biom Open Access J. 2024; 11(5): 555825. https://doi.org/10.19080/BBOAJ.2024.11.555825
LXXII. Charlwood, A., & Guenole, N. Can HR adapt to the paradoxes of artificial intelligence? Human Resource Management Journal. 2022; 32(4), 729-742. https://doi.org/10.1111/1748-8583.12433
LXXIII. Venugopal, M., Madhavan, V., Prasad, R., & Raman, R. Transformative AI in human resource management: enhancing workforce planning with topic modeling. Cogent Business & amp; Management. 2024; 11(1). doi:10.1080/23311975.2024.2432550.
LXXIV. Prikshat, V., Patel, P., Varma, A., & Ishizaka, A. A multi-stakeholder ethical framework for AI-augmented HRM. International Journal of Manpower. 2022; 43(1), 226-250. https://doi.org/10.1108/IJM-03-2021-0118.
LXXV. Rubin, M., Arnon, H., Huppert, J. D., & Perry, A. Considering the Role of Human Empathy in AI-Driven Therapy. JMIR mental health. 2024; 11, e56529. https://doi.org/10.2196/56529.
LXXVI. Jawad, L. A. Security and Privacy in Digital Healthcare Systems: Challenges and Mitigation Strategies. Abhigyan. 2024; 42(1), 23-31. Doi:10.1177/09702385241233073.
LXXVII. Conduah, A. K., Ofoe, S., & Siaw-Marfo, D. Data privacy in healthcare: Global challenges and solutions. Digital health. 2025; 11, 20552076251343959. https://doi.org/10.1177/20552076251343959.
LXXVIII. Williamson, S. M., & Prybutok, V. Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare. Applied Sciences. 2024; 14(2), 675. https://doi.org/10.3390/app14020675.
LXXIX. Khan, M. M., Shah, N., Shaikh, N., Thabet, A., & Belkhair, S. Towards secure and trusted AI in healthcare: a systematic review of emerging innovations and ethical challenges. International journal of medical informatics. 2025; 195, 105780. https://doi.org/10.1016/j.ijmedinf.2024.105780
LXXX. Sun, Q., Akman, A., & Schuller, B. W. Explainable artificial intelligence for medical applications: A review. ACM Transactions on Computing for Healthcare. 2025; 6(2), 1-31.
LXXXI. Radanliev, P. Privacy, ethics, transparency, and accountability in AI systems for wearable devices. Front. Digit. Health. 2025, 7:1431246. doi: 10.3389/fdgth.2025.1431246.
LXXXII. Hasanzadeh, F., Josephson, C.B., Waters, G. et al. Bias recognition and mitigation strategies in artificial intelligence healthcare applications. npj Digit. Med. 2025; 8, 154. https://doi.org/10.1038/s41746-025-01503-7
LXXXIII. Dehghani, F., Malik, N., Lin, J., Bayat, S., and Bento, M. "Fairness in Healthcare: Assessing Data Bias and Algorithmic Fairness," 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM), Antigua, Guatemala., 2024; pp. 1-6, doi: 10.1109/SIPAIM62974.2024.10783630.
LXXXIV. Majrashi, K. Employees’ perceptions of the fairness of AI-based performance prediction features. Cogent Business & Management. 2025; 12(1). https://doi.org/10.1080/23311975.2025.2456111
LXXXV. Nittas, V., Daniore, P., Chavez, S. J., & Wray, T. B. Challenges in implementing cultural adaptations of digital health interventions. Communications medicine. 2024; 4(1), 7. https://doi.org/10.1038/s43856-023-00426-2
LXXXVI. Alotaibi, N., Wilson, C.B. & Traynor, M. Enhancing digital readiness and capability in healthcare: a systematic review of interventions, barriers, and facilitators. BMC Health Serv Res. 2025; 25, 500. https://doi.org/10.1186/s12913-025-12663-3
LXXXVII. Werder, K., Cao, L., Park, E.H., Ramesh, B. Why AI Monitoring Faces Resistance and What Healthcare Organizations Can Do About It: An Emotion-Based Perspective. J Med Internet Res 2025;27:e51785. doi: 10.2196/51785
LXXXVIII. Khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., & Abdullah, S. Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. Biomedical materials & devices (New York, N.Y.). 2023; 1–8. https://doi.org/10.1007/s44174-023-00063-2
LXXXIX. Dalky, A., Osaid Malkawi, R. M., Alrawashdeh, A., ALBashtawy, M., & Hani, S. B. Perceptions, barriers, and risk of artificial intelligence Among healthcare professionals: A cross-sectional study. Digital health. 2025; 11, 20552076251360924. https://doi.org/10.1177/20552076251360924
XC. Byberg, E., and Crimi, M. Preparing hospitals and health organizations for AI: practical guidelines for the required infrastructure. Front Digit Health. 2025; 7:1605006. doi: 10.3389/fdgth.2025.1605006.
XCI. Nastoska, A., Jancheska, B., Rizinski, M., & Trajanov, D. Evaluating Trustworthiness in AI: Risks, Metrics, and Applications Across Industries. Electronics. 2025; 14(13), 2717. https://doi.org/10.3390/electronics14132717
XCII. Szadeczky, T., Bederna, Z. Risk, regulation, and governance: evaluating artificial intelligence across diverse application scenarios. Secur J. 2025; 38, 35 https://doi.org/10.1057/s41284-025-00495-z
XCIII. Singhal, A., Neveditsin, N., Tanveer, H., & Mago, V. Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review. JMIR medical informatics. 2024; 12, e50048. https://doi.org/10.2196/50048