🤖 AI Summary
This study addresses critical challenges in clinical AI development—namely, the difficulty of acquiring high-quality data, poor timeliness, insufficient multi-center collaboration, and elevated ethical and regulatory risks. We propose a sustainably updated, multi-center clinical imaging and data automation acquisition framework. The framework integrates a real-time streaming acquisition system, a dynamic information governance protocol, a privacy-preserving data-sharing infrastructure, and a full-lifecycle ethics review mechanism, concurrently incorporating both historical and real-time clinical data. Its key innovation lies in unifying data timeliness, representativeness, and regulatory compliance: it enables secure cross-institutional collaboration while ensuring GDPR and HIPAA compliance, thereby significantly enhancing the generalizability and validation reliability of AI models in real-world clinical settings. Experimental results demonstrate that the resulting dataset improves downstream model performance by 12.3% and reduces data update latency to under four hours.
📝 Abstract
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.