Authors : Oluwasanya Luke Ogunsakin; Kevin Mukasa

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/4344y2xv

Scribd : https://tinyurl.com/5n6n2a9e

DOI : https://doi.org/10.38124/ijisrt/26jan367

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper explores the social issues, ethical and compliance concerns relating to the implementation of AI-driven health cloud technologies in the United States. It tries to figure out how regulations, ethics and stakeholder trust are set up even in case they are unpredictable and examines innovation and value creation opportunities. In theory, the study is a synthesis of the literature available on the subject that identifies the key advantages and limitations of AI health cloud application. The advantages include more transparent clinical processes, more accurate surveillance of the health of the population, and more responsive individual treatment. The key issues are ambiguous legal standards, ethical and regulatory breaches, the lack of patient control over information, and poor privacy. The paper finds that U.S. AI health cloud programs need to integrate innovation and compliance, and advocate policy reforms that would align ethics, regulations, and compliance. It calls to action the inclusion of ethics and compliance to spur policy change in the U.S. AI cloud space. The research recommends that policymakers in health systems and leaders should work together to develop meaningful, moral, and viable policies.

Keywords : AI Health Cloud, Ethics, Regulation, Innovation in Healthcare, Governance and Digital Health.

References :

1. Agapito, G., & Cannataro, M. (2023). An overview on the challenges and limitations using cloud computing in healthcare corporations. Big Data and Cognitive Computing, 7(2), 68.
2. Ali, H., & Aysan, A. F. (2025). Ethical dimensions of generative AI: A cross-domain analysis using machine learning structural topic modeling. International Journal of Ethics and Systems, 41(1), 3–34.
3. Amugongo, L. M., Kriebitz, A., Boch, A., & Lütge, C. (2025). Operationalising AI ethics through the agile software development lifecycle: A case study of AI-enabled mobile health applications. AI and Ethics, 5(1), 227–244.
4. Arigbabu, A. T., Olaniyi, O. O., Adigwe, C. S., Adebiyi, O. O., & Ajayi, S. A. (2024). Data governance in AI-enabled healthcare systems: A case of the project Nightingale. Asian Journal of Research in Computer Science, 17(5), 85–107.
5. Babalola, O., Adedoyin, F. A., Ogundipe, F., Folorunso, A., & Nwatu, C. E. (2024). Policy framework for cloud computing: AI, governance, compliance and management. Global Journal of Engineering and Technology Advances, 21(2), 114–126.
6. Bhardwaj, A. (2024). Cyber investigations of smart devices. CRC Press.
7. Bouderhem, R. (2024). Shaping the future of AI in healthcare through ethics and governance. Humanities and Social Sciences Communications, 11(1), 1–12.
8. Du, H., Liu, M., Liu, N., Li, D., Li, W., & Xu, L. (2024). Scheduling of low-latency medical services in healthcare cloud with deep reinforcement learning. Tsinghua Science and Technology, 30(1), 100–111.
9. Georgiou, D., & Lambrinoudakis, C. (2020, September). Cloud computing framework for e-health security requirements and security policy rules case study: A European cloud-based health system. In International Conference on Trust and Privacy in Digital Business (pp. 17–31). Springer International Publishing.
10. Gerke, S. (2021). Health AI for good rather than evil? The need for a new regulatory framework for AI-based medical devices. Yale Journal of Health Policy, Law, and Ethics, 20, 432.
11. Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial intelligence in healthcare (pp. 295–336). Academic Press.
12. Gomase, V. S., Ghatule, A. P., Sharma, R., Sardana, S., & Dhamane, S. P. (2025). Cloud computing facilitating data storage, collaboration, and analysis in global healthcare clinical trials. Reviews on Recent Clinical Trials.
13. Karimian, G., Petelos, E., & Evers, S. M. (2022). The ethical issues of the application of artificial intelligence in healthcare: A systematic scoping review. AI and Ethics, 2(4), 539–551.
14. Kokala, A. (2022). The intersection of explainable AI and ethical decision-making: Advancing trustworthy cloud-based data science models. International Journal of All Research Education & Scientific Methods, 10(12), 2166–2183.
15. Liaw, S. T., Liyanage, H., Kuziemsky, C., Terry, A. L., Schreiber, R., Jonnagaddala, J., & de Lusignan, S. (2020). Ethical use of electronic health record data and artificial intelligence: Recommendations of the primary care informatics working group of the international medical informatics association. Yearbook of Medical Informatics, 29(1), 51–57.
16. Lichtenheim, G. (2024). Transforming e-governance with cloud-based AI: A systems methodology for implementation (Doctoral dissertation, Stevens Institute of Technology).
17. Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S. S., ... & Milstein, A. (2021). Ethical issues in using ambient intelligence in healthcare settings. The Lancet Digital Health, 3(2), e115–e123.
18. Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., ... & Gibson, J. (2021). Artificial intelligence for good health: A scoping review of the ethics literature. BMC Medical Ethics, 22(1), 14.
19. Najana, M., & Ranjan, P. (2024). Compliance and regulatory challenges in cloud computing: A sector-wise analysis. International Journal of Global Innovations and Solutions, 1, 1–21.
20. Palaniappan, K., Lin, E. Y. T., & Vogel, S. (2024, February). Global regulatory frameworks for the use of artificial intelligence (AI) in the healthcare services sector. In Healthcare (Vol. 12, No. 5, p. 562). MDPI.
21. Prakash, S., Balaji, J. N., Joshi, A., & Surapaneni, K. M. (2022). Ethical conundrums in the application of artificial intelligence (AI) in healthcare—A scoping review of reviews. Journal of Personalized Medicine, 12(11), 1914.
22. Prakash, S., Malaiyappan, J. N. A., Thirunavukkarasu, K., & Devan, M. (2024). Achieving regulatory compliance in cloud computing through ML. Advanced International Journal of Multidisciplinary Research, 2(2).
23. Rangel, M. A. (2021). Military breaking boundaries: Implementing third-party cloud computing practices for data storage (Doctoral dissertation, Walden University).
24. Rehan, H. (2023). AI-powered genomic analysis in the cloud: Enhancing precision medicine and ensuring data security in biomedical research. Journal of Deep Learning in Genomic Data Analysis, 3(1), 37–71.
25. Salako, A. O., Fabuyi, J. A., Aideyan, N. T., Selesi-Aina, O., Dapo-Oyewole, D. L., & Olaniyi, O. O. (2024). Advancing information governance in AI-driven cloud ecosystem: Strategies for enhancing data security and meeting regulatory compliance. Asian Journal of Research in Computer Science, 17(12), 66–88.
26. Saratkar, S., & Langote, M. (2024, October). Navigating the cloud: Enhancing healthcare through opportunities and challenges of cloud computing. In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 1045–1050). IEEE.
27. Shah, V., & Konda, S. R. (2022). Cloud computing in healthcare: Opportunities, risks, and compliance. Revista Española de Documentación Científica, 16(3), 50–71.
28. Singh, K. (2023). Artificial intelligence & cloud in healthcare: Analyzing challenges and solutions within regulatory boundaries. SSRG International Journal of Computer Science and Engineering, 10(9), 1–9.
29. Sonani, R., & Govindarajan, V. (2025). Cloud integrated governance-driven reinforcement framework for ethical and legal compliance in AI-based regulatory enforcement. Journal of Selected Topics in Academic Research, 1(1).
30. Sun, L., Jiang, X., Ren, H., & Guo, Y. (2020). Edge-cloud computing and artificial intelligence in internet of medical things: Architecture, technology and application. IEEE Access, 8, 101079–101092.
31. Williamson, S. M., & Prybutok, V. (2024). Balancing privacy and progress: A review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Applied Sciences, 14(2), 675.
32. Yadav, N., Pandey, S., Gupta, A., Dudani, P., Gupta, S., & Rangarajan, K. (2023). Data privacy in healthcare: In the era of artificial intelligence. Indian Dermatology Online Journal, 14(6), 788–792.
33. Yandrapalli, V. K., & Sharma, S. (2025). Navigating the ethical and legal aspects of cloud-based systems. In Cloud computing for smart education and collaborative learning (p. 188).
34. Zhang, J., & Zhang, Z. M. (2023). Ethics and governance of trustworthy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23(1), 7.