构建可自动更新的临床影像数据采集框架，保障AI训练数据时效性与代表性。

In this report a framework for the collection of clinical images and data for use when training and validating artificial intelligence (AI) tools is described. The report contains not only information about the collection of the images and clinical data, but the ethics and information governance processes to consider ensuring the data is collected safely, and the infrastructure and agreements required to allow for the sharing of data with other groups. A key characteristic of the main collection framework described here is that it can enable automated and ongoing collection of datasets to ensure that the data is up-to-date and representative of current practice. This is important in the context of training and validating AI tools as it is vital that datasets have a mix of older cases with long term follow-up such that the clinical outcome is as accurate as possible, and current data. Validations run on old data will provide findings and conclusions relative to the status of the imaging units when that data was generated. It is important that a validation dataset can assess the AI tools with data that it would see if deployed and active now. Other types of collection frameworks, which do not follow a fully automated approach, are also described. Whilst the fully automated method is recommended for large scale, long-term image collection, there may be reasons to start data collection using semi-automated methods and indications of how to do that are provided.