This review article discusses the clinical applications of artificial intelligence (AI) in medical imaging and image processing, highlighting recent advancements and their impact on diagnostic accuracy, treatment planning, and patient care. AI, including machine learning (ML) and deep learning, is increasingly used in medical imaging to analyze images, detect abnormalities, and improve diagnostic efficiency. Key applications include image segmentation, disease detection, image preprocessing, personalized treatment planning, predictive analytics, and quality control. Radiomics, which extracts quantitative features from medical images, plays a significant role in AI-based analysis, particularly in identifying texture patterns that provide diagnostic insights. The article covers various imaging modalities, including MRI, CT, X-ray, ultrasound, and mammography, and discusses specific AI applications such as detecting brain metastases using MRI, improving chest X-ray foreign object detection, and enhancing liver disease diagnosis through deep learning. It also explores the use of AI in breast cancer detection, prostate cancer segmentation, and the prediction of cancer response to chemotherapy. Additionally, the review highlights the potential of AI in improving diagnostic accuracy, reducing workload for healthcare professionals, and enabling more personalized treatment strategies. The study also addresses challenges such as data integrity, algorithm transparency, and ethical considerations in AI applications. Overall, the integration of AI in medical imaging is transforming healthcare by enhancing diagnostic precision, improving treatment outcomes, and optimizing clinical workflows. The review emphasizes the importance of continued research, technological innovation, and collaboration between medical professionals and AI developers to fully realize the potential of AI in medical imaging.This review article discusses the clinical applications of artificial intelligence (AI) in medical imaging and image processing, highlighting recent advancements and their impact on diagnostic accuracy, treatment planning, and patient care. AI, including machine learning (ML) and deep learning, is increasingly used in medical imaging to analyze images, detect abnormalities, and improve diagnostic efficiency. Key applications include image segmentation, disease detection, image preprocessing, personalized treatment planning, predictive analytics, and quality control. Radiomics, which extracts quantitative features from medical images, plays a significant role in AI-based analysis, particularly in identifying texture patterns that provide diagnostic insights. The article covers various imaging modalities, including MRI, CT, X-ray, ultrasound, and mammography, and discusses specific AI applications such as detecting brain metastases using MRI, improving chest X-ray foreign object detection, and enhancing liver disease diagnosis through deep learning. It also explores the use of AI in breast cancer detection, prostate cancer segmentation, and the prediction of cancer response to chemotherapy. Additionally, the review highlights the potential of AI in improving diagnostic accuracy, reducing workload for healthcare professionals, and enabling more personalized treatment strategies. The study also addresses challenges such as data integrity, algorithm transparency, and ethical considerations in AI applications. Overall, the integration of AI in medical imaging is transforming healthcare by enhancing diagnostic precision, improving treatment outcomes, and optimizing clinical workflows. The review emphasizes the importance of continued research, technological innovation, and collaboration between medical professionals and AI developers to fully realize the potential of AI in medical imaging.