Facilitating the use of routine data to evaluate artificial intelligence solutions: lessons from the NIHR/RCR data...

Radiology currently stands at the forefront of artificial intelligence (AI) development and deployment over many other medical subspecialities within the scope of both research and clinical practice. Given this current leadership position, it is imperative that we foster collaboration and knowledge sharing to ensure the ethical, responsible and effective continued progress of AI technologies in our field, ultimately leading to enhanced patient care. To achieve this objective, three workshops have been planned through a coordinated effort by the NIHR/RCR committee. These workshops aim to convene key stakeholders including eminent academics, departmental leaders and industry partners to provide insights from their own experiences and strategies to overcome common challenges faced. In this article, we describe the outcomes from the first workshop, which addresses the topic of "facilitating the use of routine data to evaluate AI solutions". The main key insights uncovered include the need for ethical considerations, detailing of methods for data curation and storage depending on the need and requirements for de-identification. We provide resources for how to de-identify data and also a list of concerns to think about before curating your data. Finally, we address secure data-sharing methods and explore the need for quality assurances, the role of the data access committee and the patient perspectives in this task.