The field of medical imaging analysis is witnessing significant advancements with the integration of vision-language models and foundation models. Recent studies have focused on adapting these models to specific medical domains, such as endoscopic surgery and medical ultrasound image analysis, to improve their performance and overcome the challenges posed by the unique characteristics of medical images. The development of new datasets and benchmarks, such as those for surgical skill assessment and retinal OCT image analysis, is also facilitating the creation of more accurate and robust AI systems for medical imaging analysis. Noteworthy papers include:

• Challenging Vision-Language Models with Surgical Data, which highlights the need for further advancements in vision-language models for surgical tasks.
• Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis, which presents a domain adaptation method for improving the performance of vision-language foundation models in ultrasound image analysis.
• MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis, which introduces a novel multimodal foundation model for retinal OCT image analysis and proposes a new evaluation benchmark.