1st Workshop on Multimodal Large Language Models (MLLMs) in Clinical Practice

Recent advancements in medical multimodal large language models (MLLMs), such as MedGemini, have introduced a transformative era in clinical AI, enabling the integration of various data modalities like 2D/3D medical images, text, and DNA sequences for more comprehensive diagnostics and personalized care. While these models show promise, challenges such as data scarcity, privacy concerns, and the need for more comprehensive evaluation metrics beyond accuracy must be addressed to fully realize their potential. MLLMs also offer exciting opportunities for enhanced human-AI collaboration in clinical workflows, improving diagnostic accuracy and decision-making. To facilitate research in this emerging field, we are organizing a workshop to foster discussion and collaboration on MLLM development and address the challenges of leveraging these models in clinical practice. The workshop theme includes topics but not limited to dataset construction, safety, fairness, human-AI collaboration, and new evaluation metrics for clinical MLLMs.