JMIR Publications

ABSTRACT

The amount of data generated in clinical practice is increasing substantially, which has benefitted the use of real-world data (RWD) for real-world evidence (RWE) in biomedical research. While early RWE efforts focused on structured EHR and claims data, advances in analytical methods, such as artificial intelligence, are expected to further enhance the ability to obtain deeper insights from RWD. Most recently, real-world imaging data (RWiD) has emerged as a novel and valuable resource, enabled by improvements in imaging infrastructure, data standardization, and de-identification technologies. Medical imaging is essential at multiple stages of clinical care, ranging from screening to post treatment assessment and surveillance. Medical imaging has become a key component of patient management, as it augments clinical decision-making across many medical specialties. RWiD is the retrospective collection of routinely gathered clinical imaging data. Using only radiology reports results in limited information compared to datasets that contain the actual images. Radiology reports primarily focus on clinical decision making rather than research purposes. Therefore, the actual images add value to real world datasets. However, using RWiD is challenging due to complex de-identification and harmonization, as well as requirements for file storage, file transfer, and computation. This article describes the background, challenges, and opportunities of real world imaging data with a focus on life sciences and biopharmaceutical applications.