With recent developments in medical imaging facilities, extensive medical imaging data are produced every day. This increasing amount of data provides an opportunity for researchers to develop data-driven methods and deliver better health care. However, data-driven models require a large amount of data to be adequately trained. Furthermore, there is always a limited amount of data available in each data center. Hence, deep learning models trained on local data centers might not reach their total performance capacity. One solution could be to accumulate all data from different centers into one center. However, data privacy regulations do not allow medical institutions to easily combine their data, and this becomes increasingly difficult when institutions from multiple countries are involved. Another solution is to use privacy-preserving algorithms, which can make use of all the data available in multiple centers while keeping the sensitive data private. Federated learning (FL) is such a mechanism that enables deploying large-scale machine learning models trained on different data centers without sharing sensitive data. In FL, instead of transferring data, a general model is trained on local data sets and transferred between data centers. FL has been identified as a promising field of research, with extensive possible uses in medical research and practice. This article introduces FL, with a comprehensive look into its concepts and recent research trends in medical imaging.

To evaluate the post-deployment performance of an artificial intelligence (AI) system (Aidoc) for intracranial hemorrhage (ICH) detection and assess the utility of ChatGPT-4 Turbo for automated AI monitoring. This retrospective study evaluated 332,809 head CT examinations from 37 radiology practices across the United States (December 2023-May 2024). Of these, 13,569 cases were flagged as positive for ICH by the Aidoc AI system. A HIPAA (Health Insurance Portability and Accountability Act) -compliant version of ChatGPT-4 Turbo was used to extract data from radiology reports. Ground truth was established through radiologists' review of 200 randomly selected cases. Performance metrics were calculated for ChatGPT, Aidoc and radiologists. ChatGPT-4 Turbo demonstrated high diagnostic accuracy in identifying intracranial hemorrhage (ICH) from radiology reports, with a positive predictive value of 1 and a negative predictive value of 0.988 (AUC:0.996). Aidoc's false positive classifications were influenced by scanner manufacturer, midline shift, mass effect, artifacts, and neurologic symptoms. Multivariate analysis identified Philips scanners (OR: 6.97, p=0.003) and artifacts (OR: 3.79, p=0.029) as significant contributors to false positives, while midline shift (OR: 0.08, p=0.021) and mass effect (OR: 0.18, p=0.021) were associated with a reduced false positive rate. Aidoc-assisted radiologists achieved a sensitivity of 0.936 and a specificity of 1. This study underscores the importance of continuous performance monitoring for AI systems in clinical practice. The integration of LLMs offers a scalable solution for evaluating AI performance, ensuring reliable deployment and enhancing diagnostic workflows.

Coronary computed tomography angiography (CCTA) has emerged as the leading noninvasive imaging modality for the assessment of coronary artery disease (CAD), offering high-resolution visualization of the coronary anatomy and plaque characterization. The development of fractional flow reserve derived from CCTA (FFR-CT) has further transformed the diagnostic landscape by enabling the simultaneous evaluation of both anatomical stenosis and lesion-specific ischemia. FFR-CT has demonstrated diagnostic accuracy comparable to invasive FFR. The combined use of CCTA and FFR-CT is now pivotal in a broad range of clinical scenarios, including the evaluation of stable and acute chest pain, assessment of high-risk and complex plaque features, and preoperative planning. As evidence continues to mount, CCTA and FFR-CT are positioned to become the primary gatekeepers to the cardiac catheterization laboratory, potentially reducing the number of unnecessary invasive procedures. This review highlights the growing clinical utility of FFR-CT, its integration with advanced plaque imaging, and the future potential of these technologies in redefining the management of CAD, while also acknowledging current limitations, including image quality requirements, cost, and access.