Artificial intelligence (AI) and cloud computing are revolutionizing medical imaging by enhancing diagnostic accuracy, efficiency, and accessibility. Deep learning models such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) play a crucial role in automating image classification, segmentation, and anomaly detection, reducing the reliance on manual interpretation by radiologists. However, implementing AI-driven solutions at scale requires substantial computational resources, a challenge addressed by cloud-based computing, which enables scalable and cost-effective AI deployment. Cloud platforms facilitate remote diagnostics, collaborative research, and real-time imaging analysis, making AI-powered healthcare more practical and widespread. This paper explores AI-driven computer vision applications in medical imaging, highlighting the role of cloud computing in overcoming resource limitations. It also discusses key advancements such as federated learning for privacy-preserving AI, edge AI for real-time diagnostics, and generative models for image enhancement. Additionally, challenges related to data privacy, bias, and model interpretability are examined, along with future directions in AI-driven healthcare. By integrating AI with cloud computing, medical imaging is becoming more precise, efficient, and accessible, shaping the future of diagnostic medicine.
