Session

DescriptionThe minisymposium "Advancing Medical AI: From Task-Specific Models to Scalable and Reliable Clinical AI Agents" brings together global experts to address key challenges and opportunities in medical AI. While task-specific models have driven advancements in pathology classification, image segmentation, and report generation, their limited adaptability and reasoning capabilities hinder broader clinical integration. This symposium explores how Large Language Models (LLMs) and Large Multimodal Models (LMMs) are redefining medical AI with multitask flexibility, multimodal data fusion, and scalable, transparent solutions. Speakers will discuss state-of-the-art systems like MEDITRON-70B, Med-Flamingo, MAIRA-2, and MMedAgent, which extend LLMs for tasks including disease detection, organ classification, and grounded diagnostics. Key themes include integrating biomedical knowledge into workflows, combining diverse data streams for holistic analysis, and optimizing models for real-world deployment in hospital infrastructure. The event emphasizes privacy-preserving frameworks, open-weight models, and inclusive AI systems that foster global accessibility. Through a multidisciplinary approach, the symposium aims to advance reliable, scalable, and equitable AI solutions for healthcare, highlighting innovations that address critical clinical needs while empowering diverse research communities worldwide.