Advances, Applications, and Challenges of Artificial Intelligence in Medical Imaging: A Comprehensive Mini-Review

Artificial intelligence (AI) is rapidly transforming medical imaging by enabling automated interpretation of images, workflow optimization, and advanced quantitative analysis. Over the past decade, AI techniques, including machine learning, deep learning, and convolutional neural networks, have demonstrated remarkable performance across modalities such as X-ray, computed tomography, magnetic resonance imaging, and ultrasound. These technologies assist radiologists in anomaly detection, image segmentation, classification, triage prioritization, and report generation, thereby improving diagnostic accuracy and operational efficiency. Despite these advancements, clinical integration of AI remains challenged by technical, ethical, and operational limitations, including limited generalizability across diverse patient populations, the “black-box” nature of deep learning models, data privacy concerns, regulatory ambiguities, and infrastructure constraints. Recent approaches such as explainable AI, federated learning, and multi-institutional validation studies show promise in overcoming these barriers. To fully realize AI’s potential, its deployment must be guided by ethical frameworks, robust clinical evaluation, clinician engagement, and the development of transparent, interpretable models. Overall, AI is positioned to augment human expertise in medical imaging, enhancing diagnostic precision, improving workflow efficiency, and contributing to personalized patient care.