Faculty Bibliography

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.

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