Workshop Overview
The rapid development of foundation models, particularly large language models (LLMs), has significantly advanced the capabilities of artificial intelligence in healthcare. In parallel, the increasing availability of diverse biomedical data types—ranging from medical imaging and genomics to electronic health records and wearable sensor streams—has underscored the importance of multimodal learning and fusion in achieving personalized and precise medicine.
This workshop focuses on multimodal foundation models for precision medicine, with an emphasis on the modeling, fusion, and interpretation of heterogeneous biomedical data. We aim to explore how recent advances in multimodal LLMs, vision-language pretraining, cross-modal fusion strategies, and domain knowledge integration can improve clinical decision-making, disease prediction, and treatment personalization. Key challenges such as aligning modalities with disparate semantics, ensuring interpretability, and managing privacy will also be central to our discussion.
By bringing together experts from AI, biomedical informatics, and clinical science, the 7th International Workshop on Artificial Intelligence for Precision Medicine will serve as a focused platform for interdisciplinary exchange and showcasing innovations in multimodal model development and deployment in real-world healthcare settings.
The workshop is held in conjunction with IEEE International Conference on Bioinformatics and Biomedicine 2025 (BIBM, CCF rank B). Accepted papers will be included in the corresponding proceedings.
