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Most previous surveys were conducted under ideal conditions, and the data was mostly in contribution. In reality, however, the condition of out-of-distribution dataset is more common and cannot be avoided. For example, in a dataset, the label only contains cats and dogs, but a new horse image appears in the data image. A model that does not take the OOD problem into account will only be able to classify it under a known label, even if this results in incorrect results; however, the OOD model should alert the researcher to the emergence of a new label class. As a result, this type of partial setting is gaining popularity. Furthermore, the problem of out-of-distribution is exacerbated in the medical environment. The availability of annotated training examples remains a significant barrier to advancement in medical imaging processing. Because experts spend so much time on annotation, the entire process is prohibitively expensive. Furthermore, once a new model has been trained, the acquisition parameters will be altered. As a result, out-of-distribution detection strategy is a critical technique in the field of medical images. Our research focuses on the OOD model in the context of medical images. First, we summarized the OOD models that have been proposed in recent years. Some of them proposed new metrics to set the standard for the model's good or bad performance, while others proposed new methods to continuously increase the model's precision, so that the model could correctly identify the OOD situation. Following that, we will concentrate on using the OOD model in medical images. We are convinced that medical images outside of the original dataset are extremely important for future research into this disease. Finally, we classify the evaluation criteria, methods, and datasets that are commonly used in OOD model training. It is hoped that our research will be beneficial to future researchers.
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