On September 2, foundational models (FMs) emerged as the cornerstone of medical AI applications in clinical settings. However, three significant hurdles impede their full potential in diagnostics and therapeutics: reliance solely on single-modal data, absence of prospective validation, and disregard for human-machine collaboration. These challenges hinder the seamless transition of FMs from the laboratory to real-world clinical practice.