The recent advancements in medical imaging AI are significantly enhancing the efficiency and accuracy of clinical workflows. There is a notable shift towards the development and deployment of pre-trained, domain-specific foundation models that reduce the need for extensive labeled data and computational resources. These models, which span various medical imaging modalities such as radiology, histopathology, and dermatology, are being fine-tuned for specific tasks, demonstrating superior performance and efficiency. Additionally, there is a growing emphasis on integrating multi-view perception and knowledge enhancement techniques to improve the accuracy and reliability of diagnostic reports. The field is also witnessing the scaling of existing frameworks to handle more complex imaging data, such as CBCT, with modifications tailored to specific challenges. Platforms are emerging that provide robust infrastructure and quality management systems to facilitate the deployment of these AI solutions in clinical settings, ensuring compliance and accessibility.

Noteworthy papers include one that introduces a framework for radiology images using lesion-enhanced contrastive learning, outperforming existing models with greater efficiency, and another that presents a novel approach to CT report generation by integrating multi-view perception and knowledge enhancement, surpassing previous state-of-the-art models.