The technical achievements of the cooperation between the National University of Singapore and other institutions have recently been published in the Journal of neurobiology nature neuroscience. This study introduces the AI meta learning method into the field of neuroscience and medicine for the first time, which can train reliable AI models on limited medical data and improve the effect of precision medicine based on brain imaging. Brain imaging technology can directly observe the neurochemical changes of the brain during information processing and response to stimuli. Theoretically, AI model based on brain imaging can be applied to predict some characterization characteristics of individuals, so as to promote accurate medical treatment for individuals. Although there are large-scale human neuroscience data sets such as UK Biobank, small-scale data samples of dozens to hundreds of people are still normal when studying clinical populations or solving key neuroscience problems. Therefore, when the amount of accurately labeled medical data is limited, how to train a reliable AI model is becoming a focus issue in the field of neuroscience and computer science. Researchers propose to use meta learning in the field of machine learning to solve the above problems. Meta learning is one of the most popular learning methods in the past few years. Its goal is to enable the model to quickly learn new tasks on the basis of acquiring existing knowledge. Through the analysis of previous small sample data, researchers found that there is an internal correlation between individual cognitive, mental health, demographic and other health attributes and brain imaging data. Based on the correlation between small sample data and large data sets, researchers propose a meta matching method to migrate the machine learning model trained on large data sets to small data sets, so as to train a more reliable model. This new method has been evaluated on the data set of British biological bank and human connectome project, which shows a higher accuracy than the traditional method. Experiments show that this new training framework is very flexible and can be combined with any machine learning algorithm. It can also effectively train AI prediction models with good generalization performance on small-scale data sets. (Xinhua News Agency)