Abstract: The convergence of cloud computing and artificial intelligence (AI) is driving a paradigm shift in healthcare, offering scalable, efficient, and innovative solutions to some of the industry's most pressing challenges. By leveraging the vast computational power and storage capabilities of cloud platforms, AI applications in healthcare are now able to process large volumes of data in real-time, delivering actionable insights for diagnostics, predictive analytics, and personalized medicine. This integration has the potential to significantly improve patient outcomes by enabling early detection of diseases, optimizing treatment plans, and enhancing operational efficiency within healthcare systems.
This paper delves into the diverse applications of cloud-based AI, from analyzing medical imaging to enabling real-time patient monitoring. It also examines the inherent challenges of this synergy, including data security and privacy concerns, regulatory compliance with standards like HIPAA and GDPR, and latency issues in time-critical applications. Supported by data visualizations, case studies, and comparative analyses, this study provides a comprehensive overview of the current state of cloud-AI adoption in healthcare. Finally, the paper highlights emerging technologies such as edge computing, blockchain, and advanced encryption protocols as potential solutions to address these challenges. Recommendations are provided for optimizing cloud-AI implementations to achieve transformative impacts, ensuring a secure, efficient, and patient-centric approach to modern healthcare.
Keywords: Cloud Computing, Artificial Intelligence, Healthcare, Scalability, Predictive Analytics, Data Security, Compliance.
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DOI:
10.17148/IJARCCE.2022.111162
