This research paper reviews and examines the increasing use of artificial intelligence (AI) in advanced medical imaging. It specifically concentrates on deep learning techniques for image reconstruction in modalities such as MRI, CT, and PET. The study discusses the workflows, technical developments, clinical applications, and challenges associated with AI-driven medical imaging. It explores various neural network architectures, data preparation methods, and loss functions used in this domain. The paper also highlights the potential for AI to improve imaging speed, reduce radiation exposure, and enhance image quality. Ultimately, the review emphasizes AI's capacity to advance medical imaging, paving the way for better clinical diagnosis and treatment, while acknowledging existing limitations such as interpretability and generalizability.

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