A Comprehensive Survey on Secure Healthcare Data Management: Integrating Cloud Storage and AI-Driven Analytics

The rapid digitization of healthcare systems has resulted in a significant increase in the quantity of sensitive patient data, requiring strong solutions for safe storage and sophisticated analytics. This work offers an in-depth examination of safe healthcare data management, emphasizing the use of cloud storage and AI-powered analytics. Cloud computing provides scalable and economical storage options; however, it also presents considerable security and privacy issues, especially for the management of sensitive health information. This work examines advanced encryption approaches, access control mechanisms, and data integrity methods intended to protect healthcare data in cloud settings. Furthermore, it investigates the function of AI-driven analytics in deriving actionable insights from extensive datasets, facilitating personalized medicine, predictive diagnoses, and enhanced patient outcomes. The intersection of AI and healthcare data prompts apprehensions over data integrity, prejudice, and ethical considerations. This assessment emphasizes upcoming concepts such federated learning, homomorphic encryption, and blockchain-based solutions, which seek to reconcile data usefulness with privacy protection. Additionally, it examines legislative frameworks such as GDPR and HIPAA and their influence on healthcare data management practices. This work seeks to provide a path for academics and practitioners by synthesizing recent accomplishments and pinpointing existing problems in the development of safe, efficient, and ethical healthcare data management systems. The amalgamation of cloud storage with AI-driven analytics has significant promise to transform healthcare; yet, it necessitates a multidisciplinary strategy to mitigate related risks and establish trustworthiness within the digital health ecosystem.