Building Secure and Equitable Enterprise and Healthcare Systems through AI Cloud and Machine Learning
DOI:
https://doi.org/10.15680/IJCTECE.2023.0605011Keywords:
Artificial intelligence, machine learning, cloud computing, enterprise systems, healthcare systems, secure access, equitable access, data privacy, ethical AIAbstract
The convergence of artificial intelligence (AI), machine learning (ML), and cloud computing has significantly transformed enterprise and healthcare systems by enabling intelligent decision-making, scalable data processing, and automation of complex workflows. These technologies enhance operational efficiency, predictive analytics, and personalized services across domains such as business management, clinical diagnostics, patient care, and public health. However, the rapid adoption of AI-driven cloud systems also introduces critical challenges related to data security, privacy, ethical use, and equitable access to services. This research examines the design and implementation of AI, cloud, and machine learning-enabled enterprise and healthcare systems with a strong emphasis on secure and equitable access. The study analyzes architectural frameworks, AI-driven analytics, and cloud-based service models while integrating secure software engineering practices and fairness-aware AI techniques. A comprehensive research methodology involving system modeling, simulation, and performance evaluation is employed to assess efficiency, scalability, security, and accessibility. The findings demonstrate that combining AI and cloud technologies with robust security controls and equity-focused design principles improves service delivery, protects sensitive data, and reduces digital disparities. This research contributes practical guidelines for developing trustworthy, inclusive, and secure AI-driven enterprise and healthcare systems.
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