The striding advances of computer vision techniques are revolutionizing many long-standing automatic medical diagnosis tasks. Emerging trends—such as Large Language Models (LLMs), Foundation Models (FMs), advanced learning paradigms (e.g., un-/semi-/self-supervised learning), and considerations of fairness and generalization—remain underexplored for secure and reliable automated medical diagnosis. Distinctly, this workshop emphasizes integrating insights from clinicians and radiologists alongside technical discussions to better advance the field.