This poster presents an AI-driven multimodal workflow for breast cancer designed to enhance late-phase clinical trial outcome prediction. By augmenting small early-phase cohorts with multimodal foundation models and real-world data, the approach demonstrates improved alignment with Phase III outcomes. These results highlight the potential of multimodal modeling to extract meaningful signals from limited early-phase data, supporting more informed development decisions earlier in the pipeline.