Domain adaptation (DA) is a technique that uses the knowledge from similar data sets to enhance the generalizability of a model, which proves to be effective in addressing the issue of limited training data. However, due to the complexity of medical data, most advances have occurred in the natural domain and not in the medical field. Furthermore, the majority of advancements are derived from conventional DA datasets, which may introduce result bias. This article presents a comprehensive new analysis of four widely used DA algorithms, tested on more realistic medical data sets to assess the potential of these DA techniques. The examination covers an evaluation of their model performance, discrepancies in data distribution, and the interpretability of the models. For example, the Deep subdomain adaptation network (DSAN) achieves a high level of accuracy on the COVID-19 dataset (89.9%) using Resnet34. Furthermore, the interpretability of the DSAN’s prediction using the skin cancer dataset is implausible. In conclusion, we offer perspectives on the outcomes obtained. Our codes are available at https://github.com/AIPMLab/Domain_Adaptation.