The field of medical visual question answering and multimodal analysis is rapidly evolving, with a focus on developing more accurate and efficient models for clinical diagnosis and decision-making. Recent research has highlighted the importance of incorporating specialized medical knowledge and domain adaptation into large language models and vision-language models. This has led to the development of innovative frameworks and architectures that can effectively integrate multimodal data, such as images, text, and clinical information, to improve diagnostic accuracy and patient outcomes. Notable advancements include the use of agentic frameworks, knowledge graph retrieval, and hierarchical semantic prompts to enhance model performance and interpretability. Furthermore, the application of multimodal analysis to specific clinical tasks, such as carotid risk stratification and intraoperative pathology, has shown promising results. Overall, these developments have the potential to transform the field of medical imaging and diagnostics, enabling more accurate and personalized patient care.

Noteworthy papers include: AMANDA, which proposes a training-free agentic framework for medical knowledge augmentation in medical visual question answering. DuPLUS, which introduces a novel vision-language framework for universal medical image segmentation and prognosis. GROK, which presents a grounded multimodal large language model for clinician-grade diagnoses of ocular and systemic disease. CRISP, which develops a clinical-grade foundation model for intraoperative pathology, demonstrating robust generalization and high diagnostic accuracy in real-world conditions.