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There has been an exponential rise in artificial intelligence (AI) research in imaging in recent years. While the dissemination of study data that has the potential to improve clinical practice is welcomed, the level of detail included in early AI research reporting has been highly variable and inconsistent, particularly when compared to more traditional clinical research. However, inclusion checklists are now commonly available and accessible to those writing or reviewing clinical research papers. AI-specific reporting guidelines also exist and include distinct requirements, but these can be daunting for radiologists new to the field. Given that pediatric radiology is a specialty faced with workforce shortages and an ever-increasing workload, AI could help by offering solutions to time-consuming tasks, thereby improving workflow efficiency and democratizing access to specialist opinion. As a result, pediatric radiologists are expected to be increasingly leading and contributing to AI imaging research, and researchers and clinicians alike should feel confident that the findings reported are presented in a transparent way, with sufficient detail to understand how they apply to wider clinical practice. In this review, we describe two of the most clinically relevant and available reporting guidelines to help increase awareness and engage the pediatric radiologist in conducting AI imaging research. This guide should also be useful for those reading and reviewing AI imaging research and as a checklist with examples of what to expect.

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare.

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare.