Session

DescriptionArtificial intelligence (AI) has achieved major success in medicine, particularly in diagnostic imaging, pathology classification, and clinical report generation, accelerating research translation and improving care. However, most deployed systems remain task-specific, lack biomedical reasoning, and generalize poorly across data modalities and clinical settings. The Advancing Medical AI minisymposium explores how emerging approaches, especially large multimodal models (LMMs), can enable AI-driven in-silico clinical trials (ISCTs) that better connect research innovation with clinical application. Recent LMMs integrate medical images, text, and structured data to support diagnosis, segmentation, and reporting, enabling the simulation of biological and clinical processes and advancing virtual patient modeling. Key challenges remain in explainability, computational efficiency, privacy protection, and integration with hospital infrastructure, highlighting the need for transparent data governance and verifiable systems. In parallel, ISCTs are gaining momentum as computer-based experiments that model disease progression and therapy response in virtual patient cohorts. Built on digital twins-dynamic computational models continuously updated with clinical data, ISCTs promise lower costs, faster development, and improved safety. Despite their potential, barriers such as data heterogeneity, limited interpretability, validation gaps, regulatory constraints, and infrastructure demands persist.