The field of medical image analysis is rapidly evolving, with a focus on developing scalable and automated methods for image quality assessment and diagnostic decision-making. Recent developments have centered around the creation of comprehensive foundation models that can handle variability in image dimensions, modalities, and anatomical regions. These models have shown significant promise in improving diagnostic accuracy and advancing clinical workflows. Furthermore, collaborative platforms are being developed to facilitate interdisciplinary collaboration among clinicians, researchers, and AI developers, aiming to accelerate the translation of AI research into impactful clinical solutions. Noteworthy papers in this area include:

• MedIQA, which introduces a scalable foundation model for medical image quality assessment, significantly outperforming baselines in multiple downstream tasks.
• MAIA, a collaborative medical AI platform that facilitates interdisciplinary collaboration and supports real-world use cases in medical imaging AI.
• Detection of Medial Epicondyle Avulsion in Elbow Ultrasound Images via Bone Structure Reconstruction, which proposes a reconstruction-based framework for detecting medial epicondyle avulsion in elbow ultrasound images, achieving high accuracy and outperforming existing approaches.
• Annotation-Free Human Sketch Quality Assessment, which proposes a generic method for sketch quality assessment without the need for specific quality annotations from humans, with potential applications in image quality assessment and noisy label cleansing.