Adversarial attacks pose a significant threat to the robustness and reliability of machine learning models in medical-related tasks. These attacks involve the deliberate manipulation of input data to deceive models into making incorrect predictions, often by introducing subtle perturbations that lead to drastic changes in predicted outcomes. In the medical domain, where machine learning models are increasingly relied upon for disease diagnosis and treatment recommendation, the consequences of such attacks can be severe, potentially leading to misdiagnosis or inappropriate clinical decisions. Research has been conducted into building defense systems to safeguard machine learning models from adversarial attacks. Recently, Shi et al. proposed the Zero-shot Image Purification (ZIP) framework that leverages diffusion models to transform adversarially-attacked images back into clean ones without knowing the attack mechanism a priori. However, the datasets used in their experiments are just standard benchmark datasets (i.e., CIFAR-10, GTSRB, and Imagenette), which might differ a lot from real-world datasets. Therefore, in this paper, we evaluate the vulnerability of deep learning architectures commonly used in medical imaging and investigate the effectiveness of the ZIP framework under different attack strategies. We also fine-tune the framework parameters and the classifiers for performance enhancement. The results demonstrate that after applying image purification and model fine-tuning, the classifiers exhibit improved robustness against adversarial perturbations, where the defense accuracy increases significantly across all medical datasets and attack methods. Source code is available at: https://github.com/ELO-Lab/ZIP-medical-adversarial-defense .