Recent Advances in AI for Medical Imaging: Progress, Challenges, and Future Directions

Recent advances in artificial intelligence (AI) have significantly transformed medical imaging, enabling substantial progress in image acquisition, reconstruction, diagnosis, prognosis, and clinical decision support. Advances in deep learning, foundation models, multimodal integration, and generative modeling have improved accuracy and robustness across modalities such as MRI, CT, X-ray, ultrasound, and digital pathology. Despite these successes, critical challenges remain, including limited generalizability and interpretability, data heterogeneity, privacy concerns, and barriers to real-world clinical deployment. This tutorial provides a concise and up-to-date overview of recent advances in AI for medical imaging, reviewing key paradigms such as physics-informed and interpretable learning, privacy-preserving collaborative learning, and open-source medical imaging foundation models. We further discuss open challenges and future research directions shaping the next generation of medical imaging AI.