Dear Colleagues,

Medical imaging plays a crucial role in diagnosing and monitoring diseases, providing valuable insights for clinicians. With the rapid advancement of artificial intelligence (AI) techniques, particularly deep learning algorithms, the analysis and interpretation of medical images have been revolutionized. AI has been applied in various areas of medical imaging, including image reconstruction and enhancement, machine learning for automated detection, and image segmentation and analysis.

In image reconstruction and enhancement, AI techniques such as iterative reconstruction and super-resolution can improve image quality, reduce noise, and enhance spatial resolution. This integration of AI can provide clinicians with clearer and more detailed images, enabling accurate diagnosis and treatment planning. Machine learning models have been extensively employed in medical imaging for automated detection of abnormalities. These models are trained using large datasets to detect and localize pathologies such as tumors, fractures, and cardiovascular anomalies. AI-driven automated detection systems hold immense potential for early diagnosis and improved patient outcomes. Accurate segmentation of medical images is crucial for disease detection and treatment evaluation. AI algorithms, including convolutional neural networks and graph-cut algorithms, can automate and optimize the process of image segmentation, enabling precise delineation of anatomical structures and pathological regions. This assists clinicians in efficient and reliable analysis of medical images.

However, several challenges need to be addressed in the application of AI in medical imaging. These challenges include data quality, interpretability of AI models, and regulatory concerns. Overall, AI has shown great promise in improving medical imaging.

So, research articles, reviews and studies in this area of study are welcome. We look forward to receiving your contributions.

Dr. Guoyue Chen

Section editor