Faculty Bibliography

Global imaging demand now exceeds capacity due to an aging population, rising comorbidities, expanded indications for imaging, and workforce shortages. These pressures are reshaping national health care policy. At the same time, the competing demands to expand access to imaging services while reducing costs are creating momentum to reimagine imaging delivery through the integration of transformative technologies like artificial intelligence (AI). This consensus statement from the International Society for Strategic Studies in Radiology (IS3R) is a call to action, outlining promising AI solutions that increase workflow efficiency and align radiology with health system and payer priorities. Reflecting expert consensus from the IS3R meeting in Dublin, Ireland, in August 2025, this statement defines strategic directions for radiology amid accelerating digitalization, rapid innovation, and data-driven patient care. This article covers four domains: image interpretation, capacity building, data governance, and systemic digital transformation. This article examines how AI can address rising imaging demand and costs while addressing challenges related to improving data quality, standardization, and interoperability. It also highlights the need to re-engineer workflows and adapt to new care models while maintaining high-quality services. Finally, it presents consensus recommendations outlining how AI can enable multimodal health data integration and advance alignment with value-based care.

INTRODUCTION/BACKGROUND:This study aimed to evaluate the impact of the COVID-19 pandemic on screening patterns from a large, diverse, nationally representative breast cancer screening registry and to perform microsimulation modeling of breast cancer outcomes. METHODS:Retrospective breast imaging and outcomes data from the National Mammography Database (NMD) from January 2017 through December 2022 were collected and partitioned into pre-COVID, peak-COVID, and post-COVID time periods. Microsimulation modeling was performed to predict breast cancer outcomes, including the cancer stage distribution, excess breast cancer deaths, and years of life lost from 2020 to 2065. RESULTS:There were 12,118,324 screening exams, 1,211,754 screening recalls, 194,096 biopsies, and 55,732 cancer diagnoses included for analysis. There was an acute drop in all metrics in 2020, and these metrics did not approach pre-COVID levels until 2022. This corresponded with a significant increase in the time interval between screening exams between the pre-COVID and post-COVID periods (16.3±7.7 vs. 17.8±9.3 months respectively; p<0.001). Simulations predicted that, by 2030, 12% more late-stage cancers would be diagnosed. The estimated cumulative excess deaths following peak-COVID were 6.7 per 100,000 women by 2030, an increase of 0.40%, increasing to 16.8 (0.51%) excess deaths by 2040. The corresponding number of years of life lost by 2065 was 565 per 100,000 women screened. CONCLUSIONS:The interruption in screening during peak-COVID and the slow return to pre-COVID screening levels may have resulted in an increase in late-stage cancers, excess cancer deaths, and years of life lost.

PURPOSE/OBJECTIVE:To develop the REporting checklist for FoundatIon and large laNguagE models (REFINE), an international reporting guideline for transparent and reproducible reporting of foundation model (FM) and large language model (LLM) studies in medical research, including imaging artificial intelligence (AI) applications. METHODS:The protocol was prespecified and publicly archived. A modified Delphi process was conducted to establish reporting standards for unimodal and multimodal FM and LLM applications involving text, imaging, and structured data. The steering committee coordinated protocol development, expert recruitment, all Delphi rounds, and the harmonization phase. Decisions were made based on predefined consensus thresholds. In Rounds 1 and 2, structured ratings and free-text feedback informed iterative revisions. In the post-Delphi harmonization phase, terminology was standardized, and detailed reporting instructions were finalized. RESULTS:The REFINE development group comprised 57 contributors from 17 countries, and 54 panelists from 16 countries completed Rounds 1 and 2. The harmonization phase was completed by three expert panelists and the steering committee. The entire process produced a 44-item, six-section framework with standardized terminology and detailed reporting instructions, supported by an online platform for practical use (https://refinechecklist.github.io/refine/checklist.html). CONCLUSION/CONCLUSIONS:The REFINE provides a comprehensive, consensus-based reporting standard for medical FM and LLM research, including imaging AI studies. The online version facilitates practical implementation. CLINICAL SIGNIFICANCE/CONCLUSIONS:The REFINE enables transparent, comparable, and reproducible reporting of FM and LLM studies, supporting reliable evidence synthesis in medical and imaging-focused AI studies.

Large language models (LLMs) have transformative potential in radiology, including textual summaries, diagnostic decision support, proofreading, and image analysis. However, the rapid increase in studies investigating these models, along with the lack of standardized LLM-specific reporting practices, affects reproducibility, reliability, and clinical applicability. To address this, reporting guidelines for LLM studies in radiology were developed using a two-step process. First, a systematic review of LLM studies in radiology was conducted across PubMed, IEEE Xplore, and the ACM Digital Library, covering publications between May 2023 and March 2024. Of 511 screened studies, 57 were included to identify relevant aspects for the guidelines. Then, in a Delphi process, 20 international experts developed the final list of items for inclusion. Items consented as relevant were summarized into a structured checklist containing 32 items across six key categories: general information and data input; prompting and fine-tuning; performance metrics; ethics and data transparency; implementation, risks, and limitations; and further/optional aspects. The final FLAIR (Framework for LLM Assessment in Radiology) checklist aims to standardize reporting of LLM studies in radiology, fostering transparency, reproducibility, comparability, and clinical applicability to enhance clinical translation and patient care. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. Supplemental material is available for this article.

PURPOSE/OBJECTIVE:To estimate economic and environmental reduction potential of iodinated contrast media (ICM) saving strategies, by examining supply chain data (from iodine extraction through administration) to inform a decision-making framework which can be tailored to local institutional priorities. METHODS:A 100 mL polymer vial of ICM was set as the standard reference case (SRC) for baseline comparison. To evaluate cost and emissions impacts, four ICM reduction strategies were modeled relative to this SRC baseline: vial optimization, hardware or software (AI-enabled) dose reduction, and multi-dose vial/injector systems. This analysis was then translated into a decision-making framework for radiologists to compare ICM strategies by cost, emissions, and operational feasibility. RESULTS:The supply chain life cycle of a 100 mL iodinated contrast vial produces 1,029 g CO2e, primarily from iodine extraction and clinical use. ICM-saving strategies varied widely in emissions reduction, ranging from 12%-50% nationally. Economically a 125% tariff could inflate national ICM-related costs to $11.9B, the ICM reduction strategy of AI-enhanced ICM systems could lower this expenditure to $2.7B. Institutional analysis reveals that the ICM savings from high-capital upfront investment strategies can offset their initial investment, highlighting important trade-offs for implementation decision-making. CONCLUSION/CONCLUSIONS:ICM is a major and modifiable contributor to healthcare carbon emissions. Depending on the utilized ICM-reduction strategy, emissions can be reduced by up to 53% and ICM-related costs by up to 50%. To guide implementation, we developed a decision-making framework that categorizes strategies based on environmental benefit, cost, and operational feasibility, enabling radiology leaders to align sustainability goals with institutional priorities.

A digital twin is a computational model that provides a virtual representation of a specific physical object, system, or process and predicts its behavior at future time points. These simulation models form computational profiles for new diagnosis and prevention models. The digital twin is a concept borrowed from engineering. However, the rapid evolution of this technology has extended its application across various industries. In recent years, digital twins in healthcare have gained significant traction due to their potential to revolutionize medicine and drug development. In the context of radiology, digital twin technology can be applied in various areas, including optimizing medical device design, improving system performance, facilitating personalized medicine, conducting virtual clinical trials, and educating radiology trainees. Also, radiologic image data is a critical source of patient-specific measures that play a role in generating advanced intelligent digital twins. Generating a practical digital twin faces several challenges, including data availability, computational techniques, validation frameworks, and uncertainty quantification, all of which require collaboration among engineers, healthcare providers, and stakeholders. This review focuses on recent trends in digital twin technology and its intersection with radiology by reviewing applications, technological advancements, and challenges that need to be addressed for successful implementation in the field.

Recent advances in Artificial Intelligence (AI) methodologies and their application to medical imaging has led to an explosion of related research programs utilizing AI to produce state-of-the-art classification performance. Ideally, research culminates in dissemination of the findings in peer-reviewed journals. To date, acceptance or rejection criteria are often subjective; however, reproducible science requires reproducible review. The Machine Learning Education Sub-Committee of the Society for Imaging Informatics in Medicine (SIIM) has identified a knowledge gap and need to establish guidelines for reviewing these studies. This present work, written from the machine learning practitioner standpoint, follows a similar approach to our previous paper related to segmentation. In this series, the committee will address best practices to follow in AI-based studies and present the required sections with examples and discussion of requirements to make the studies cohesive, reproducible, accurate, and self-contained. This entry in the series focuses on image classification. Elements like dataset curation, data pre-processing steps, reference standard identification, data partitioning, model architecture, and training are discussed. Sections are presented as in a typical manuscript. The content describes the information necessary to ensure the study is of sufficient quality for publication consideration and, compared with other checklists, provides a focused approach with application to image classification tasks. The goal of this series is to provide resources to not only help improve the review process for AI-based medical imaging papers, but to facilitate a standard for the information that should be presented within all components of the research study.

MRI is the most effective method for screening high-risk breast cancer patients. While current exams primarily rely on the qualitative evaluation of morphological features before and after contrast administration and less on contrast kinetic information, the latest developments in acquisition protocols aim to combine both. However, balancing between spatial and temporal resolution poses a significant challenge in dynamic MRI. Here, we propose a radial MRI reconstruction framework for Dynamic Contrast Enhanced (DCE) imaging, which offers a joint solution to existing spatial and temporal MRI limitations. It leverages a locally low-rank (LLR) subspace model to represent spatially localized dynamics based on tissue information. Our framework demonstrated substantial improvement in CNR, noise reduction and enables a flexible temporal resolution, ranging from a few seconds to 1-second, aided by a neural network, resulting in images with reduced undersampling penalties. Finally, our reconstruction framework also shows potential benefits for head and neck, and brain MRI applications, making it a viable alternative for a range of DCE-MRI exams.