The article "Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing—A Review" by Rafal Obuchowicz, Michal Strzelecki, and Adam Piórkowski provides an overview of the current state and future potential of AI in medical imaging. The authors discuss the latest advancements and applications of AI in various imaging modalities, including X-ray, MRI, CT, SPECT, and ultrasonography. Key topics include image segmentation, radiomics, deep learning, and machine learning techniques. The review highlights the benefits of AI in enhancing diagnostic accuracy, efficiency, and patient care, while also addressing challenges such as data integrity and ethical concerns. Specific applications are detailed, such as deep learning for detecting brain metastases on MRI, extended reality in diagnostic imaging, and AI integration in radiology. The article also covers the use of AI in specific organs and conditions, such as the lungs, breasts, liver, brain, and prostate, and discusses the advantages of deep learning over classical machine learning methods. Overall, the review underscores the transformative impact of AI on medical imaging and the need for further research and collaboration between radiologists and AI developers to fully realize its potential.The article "Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing—A Review" by Rafal Obuchowicz, Michal Strzelecki, and Adam Piórkowski provides an overview of the current state and future potential of AI in medical imaging. The authors discuss the latest advancements and applications of AI in various imaging modalities, including X-ray, MRI, CT, SPECT, and ultrasonography. Key topics include image segmentation, radiomics, deep learning, and machine learning techniques. The review highlights the benefits of AI in enhancing diagnostic accuracy, efficiency, and patient care, while also addressing challenges such as data integrity and ethical concerns. Specific applications are detailed, such as deep learning for detecting brain metastases on MRI, extended reality in diagnostic imaging, and AI integration in radiology. The article also covers the use of AI in specific organs and conditions, such as the lungs, breasts, liver, brain, and prostate, and discusses the advantages of deep learning over classical machine learning methods. Overall, the review underscores the transformative impact of AI on medical imaging and the need for further research and collaboration between radiologists and AI developers to fully realize its potential.