Faculty Bibliography

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.

Recent systematic reviews have raised concerns about the quality of reporting in studies evaluating the accuracy of large language models (LLMs) in medical applications. Incomplete and inconsistent reporting hampers the ability of reviewers and readers to assess study methodology, interpret results, and evaluate reproducibility. To address this issue, the MInimum reporting items for CLear Evaluation of Accuracy Reports of Large Language Models in healthcare (MI-CLEAR-LLM) checklist was developed. This article presents an extensively updated version. While the original version focused on proprietary LLMs accessed via web-based chatbot interfaces, the updated checklist incorporates considerations relevant to application programming interfaces and self-managed models, typically based on open-source LLMs. As before, the revised MI-CLEAR-LLM focuses on reporting practices specific to LLM accuracy evaluations: specifically, the reporting of how LLMs are specified, accessed, adapted, and applied in testing, with special attention to methodological factors that influence outputs. The checklist includes essential items across categories such as model identification, access mode, input data type, adaptation strategy, prompt optimization, prompt execution, stochasticity management, and test data independence. This article also presents reporting examples from the literature. Adoption of the updated MI-CLEAR-LLM can help ensure transparency in reporting and enable more accurate and meaningful evaluation of studies.

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.

As large language models (LLMs) and other generative artificial intelligence (AI) models are rapidly integrated into radiology workflows, unique pitfalls threatening their safe use have emerged. Problems with AI are often identified only after public release, highlighting the need for preventive measures to mitigate negative impacts and ensure safe, effective deployment into clinical settings. This article summarizes best practices for the safe use of LLMs and other generative AI models in radiology, focusing on three key areas that can lead to pitfalls if overlooked: regulatory issues, data privacy, and bias. To address these areas and minimize risk to patients, radiologists must examine all potential failure modes and ensure vendor transparency. These best practices are based on the best available evidence and the experiences of leaders in the field. Ultimately, this article provides actionable guidelines for radiologists, radiology departments, and vendors using and integrating generative AI into radiology workflows, offering a framework to prevent these problems.

Purpose To evaluate intravoxel incoherent motion (IVIM) biomarkers across different MRI vendors and software programs for breast cancer characterization in a two-site study. Materials and Methods This institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study included 106 patients (with 18 benign and 88 malignant lesions) who underwent bilateral diffusion-weighted imaging (DWI) between February 2009 and March 2013. DWI was performed using 1.5-T (n = 6) or 3-T MRI scanners from two vendors using single-shot spin-echo echo-planar imaging or twice-refocused, bipolar gradient single-shot turbo spin-echo readout with multiple b values between 0 and 1000 sec/mm². IVIM parameters tissue diffusivity (D_t