Real-World Imaging Data: Opportunities and Challenges

The amount of data generated in clinical practice is increasing substantially. This has benefited the use of real-world data for real-world evidence in biomedical research. While early real-world evidence efforts focused on structured electronic health record and insurance claims data, advances in analytical methods, such as AI, are expected to further enhance the ability to obtain deeper insights from real-world data. Most recently, real-world imaging data (RWiD) has emerged as a novel and valuable resource. RWiD is enabled by improvements in imaging infrastructure, data standardization, and deidentification technologies. Medical imaging is essential at multiple stages of clinical care, ranging from screening to posttreatment 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 deidentification and harmonization, as well as requirements for file storage, file transfer, and computation. This editorial paper provides an educational overview, including the background, challenges, and opportunities of RWiD, and offers examples of RWiD applications that benefit life sciences and biopharmaceutical use cases.