Artificial intelligence methods are developing rapidly in the medical field. However, the effectiveness of model training relies heavily on the support of sufficient sample sizes. Due to various constraints such as privacy, security, ethics, and costs in the medical field, it is rather difficult to obtain a large number of labeled training samples. Problems like the scarcity of rare disease cases, the lack of biological data for drug molecule mining, and the shortage of high-quality annotations for medical images significantly reduce the ability of models to learn from observed data, which in turn leads to poor prediction performance. In this context, constructing efficient learning artificial intelligence models for small sample data is of far-reaching significance both theoretically and practically. On the one hand, it can help to explore potential patterns when samples are insufficient in the early stage of new research. On the other hand, high-quality models can effectively reduce the cost of manual annotation, shorten the research cycle, and provide opportunities for solving challenging problems in medical research where it is difficult to collect a sufficient number of samples. Driven by both the expected advantages and actual needs, the research on artificial intelligence for small sample data has gradually become a highly anticipated and important research direction. This review systematically collates and summarizes the principles, advantages, disadvantages, applicable scenarios, and principal challenges associated with six artificial intelligence methods currently employed in the context of small-sample medical data, namely generative adversarial networks, graph neural networks, transfer learning, reinforcement learning, and Meta-learning. Furthermore, the review provides an extensive outlook and in-depth contemplation on the future trajectory of artificial intelligence methodologies in the realm of small sample data in medicine.

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