Transforming Healthcare: The Convergence of Generative AI and Cloud Technologies

Authors

  • Ripunjaya Pattnaik Works for Amazon Web Services Inc., USA Author

DOI:

https://doi.org/10.32628/CSEIT251112127

Keywords:

Generative Artificial Intelligence, Cloud Computing Healthcare, Personalized Medicine, Healthcare Data Privacy, Clinical Decision Support Systems

Abstract

The convergence of generative AI and cloud computing is reshaping the healthcare landscape, presenting unprecedented opportunities for innovation and advancement across multiple domains. This comprehensive article explores the transformative potential of these technologies, examining their applications in personalized medicine, clinical decision support, medical imaging, and healthcare operations. The article delves into critical research areas, including privacy-preserving AI frameworks, edge computing integration, and interoperability solutions that enable seamless data exchange across healthcare systems. Special attention is given to emerging capabilities in emergency response, population health management, and patient engagement, alongside cost optimization and quality improvement considerations. Through analysis of current implementations and future directions, this article identifies key technical challenges, ethical considerations, and success factors that will shape the evolution of AI-powered healthcare solutions. This article emphasizes the importance of cross-disciplinary collaboration, robust governance frameworks, and sustainable infrastructure development in realizing the full potential of these technologies. This article contributes to the growing body of knowledge on AI applications in healthcare while highlighting pathways for responsible innovation that prioritize patient outcomes, privacy, and healthcare system efficiency.

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References

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Published

31-01-2025

Issue

Section

Research Articles

How to Cite

Transforming Healthcare: The Convergence of Generative AI and Cloud Technologies. (2025). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 1313-1321. https://doi.org/10.32628/CSEIT251112127