Introduction: There have been notable advancements in large foundation models (FMs), which exhibit generalizable language understanding, visual recognition, and audio comprehension capabilities. These advancements highlight the potential of personalized AI assistants in efficiently assisting with daily tasks, ultimately enhancing human life.

Healthcare is one of the most crucial industries touching every individual. Yet, due to large populations and limited medical professionals, it faces significant challenges, including the high cost and low doctor-to-population ratio. This shortage is more pronounced in rural and developing regions, where access to qualified doctors is severely limited, exacerbating health disparities and preventing timely treatment for common and complex conditions alike. Hence, there is a critical need to develop effective, affordable, and professional AI-driven medical assistants.

Despite the great success in general domains, FMs struggle in specific domains requiring strict professional qualifications, such as healthcare, which has high sensitivity and security risk. In light of the growing healthcare demands, this workshop aims to explore the potential of Medical Foundation Models (MFMs) in smart medical assistance, thereby improving patient outcomes and streamlining clinical workflows. Considering the primary clinical needs, we emphasize the explainability, robustness, and security of the large-scale multimodal medical assistant, pushing forward its reliability and trustworthiness. By bringing together expertise in diverse fields, we hope to bridge the gap between industry and academia regarding precision medicine, highlighting clinical requirements, inherent concerns, and AI solutions. Through this cooperative endeavor, we aim to unlock the potential of MFMs, striving for groundbreaking advancements in healthcare.